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# jArricles



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

Therese F. Mar,1 GaryA. 

### Norris,2 

### Jane Q. 

### Koenig,1 and Timothy V. Larson3
'Department of Environmental Health, University ofWashington, Seattle, Washington, USA; 2U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA; 3Department of Civil and Environmental Engineering, University ofWashington, Seattle, Washington, USA

Weevaluated the association between mortality outcomes in elderlyindividuals 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 ofdaily 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 ofmortality. Phoenix is in the arid Southwest and has approximately 1 million residents (9.7% ofthe 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 HealthPerpect 108:347-353 (2000). [Online25 February2000] 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 coarsep;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 

### (PM10) or the coarse fraction of

### PM {PMCF (PM 10 minus PM2 ,5)]' In contrast, Osuo er 

### aI. (1J} found that 

### PMIO 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 ofapproximately 

### 1 million residents (16). 

### lc is an interesting location because ofic.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 nonaccidental deaths in the study region (based on zip codes). This may

Environmental HeCllrh Perspecrives • Volume 108, Number4, April 2000 be reflective ofthe increased si:ze ofPhoenix's elderly popularion, which is more prone to cardiovasculardisease. 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 ofPM2S 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 ofPM 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 anddara. Morrality data for aU ofMaricopa County from 

### 1995 to 

### 1997

were obtained from the Arizona Ceocer for 'Health Statistics in Phoenix. Death

Address correspondence to T.F 

### Mar, Departmem ofEnvironmental HeaJth, Box 357234, Universi£), ofWashington. 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 ofthe 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 ofWashington.

The coments ofthis paper are solely the responsibility ofrhe 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 ofreade names
or commercial products docs nor constitute an endorsementor recommendation for use.

Received 8 September 1999: accepted 9 November 1999.



### 347Arricles • Mar er al.

ccrtifi-cate data included residence zip code and the primary cause ofdeath 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 inTable 

### 1. We obtained PM2.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 approximatey 10 

### km west-northwest ofdowntown Phoenix at a state and local air monitoringstation. Standard meteorologic parameters such

as wind speed and direction, temperature, and relative humiditywere continuouslymeasured. The average temperature in Phoenix from 1995 (Q 1997 was 23.7 ± B.l"C. The average relative hwniditywas 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. TheTEOMPM10 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.Scydone on the TEOM was replaced with awell-impactor ninery-six (WINS) inlet

on 20 December 1996. TheWINS inlet has a sharpercutpoint as compared to the cyclone. We averaged the hourly concentrations to create a 24-hr average (0700--0700 hr), and

we calculated the concentration ofcoarse fraction (TEOM PMCF) as TEOM PMiO minusTEaM PM2.5. NERL investigawrs collec[ed the daily gravimetric integrated 24-hr (starring at 0700) fine particle filter samples using adual 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 quarnfilter 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.Sand 

### 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 hrfrom 0700 to 0700. Weused [he 

### maximum hourly 

### 0 3(03 max) concentration in the same 24-hrperiod in the analysis. The PM2.5 constituents that we evaluated for effects on monaiity were sulfur, zinc, lead, soil-corrected potassium 

### (Ks) (23), OC I 

## 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 

### 3presents 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 ofsoil. 

### Statistical analysis. In our zip-code regions, we analyzed a rotal of9,276 nonaccidental deaths from 1995 to 1997. Poisson regression was used ro evaluate [he association between the air pollutant exposure variables and [he mortalityoutcomes 

### (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. Theeffect ofair pollution on mortality 

### is small and can be influenced by confounders. Therefore, base models for total mortality 

### and cardiova!icular mortaJiry were consUUcted by adjustingfor day ofrheweek with. indicator variables, and time trends, temperature, and relative humidity with smoothing functions (25). We determined degrees offreedom 

### (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 ofage 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 ofpollutantconcentrations (1995-1997).
Particulate matterpollutant, year 

### PM,.s (OFPSSI

1995 1996 1997 J·yearmean 

### PMIO(TEOMI

1995 1996 1997 J·yearmean 

### PM,.,(TEOMI

199, 1996 1997 J·yearmean 

### PMCF(TEOMI

1995 1996 1997 J·yearmean 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. Number4, April 2000 • EnvironmentClI Health Perspectives

### relative humidity [Q minimize the Ale. The base model for total mortality used indicator variables for day ofthe 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 

### 0dayslag. 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 ofweek, 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 independentseries. 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 PM lO,

### TEaMPM,.5' TEaM PMcF' PM,.; (DFP5S), 5, Zn, Pb, soil, K" nonsoil 

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

Table 3. 

### Percentoftotal mass ofPMt5 

### 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

'IfS

### is assumedto be inlheform 01 (NH41,50" 

### the mllss percentwould be 151%. 1I;!.2D%AI +2.49%

### Si+1.63% 

### ea +2.42% 

### Fa +**1.9~%**Ti (23).



### Table 5. Correlation coefficientmatrix ofairpollufants.



### 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 PMlOa 

### PM2Si! 

### PMC'Fa Nonsoil PMz.s Soil CO NO, OJ 

# °Oma,
S 1 

### 118asedon TEOM measuremenls. Articles • Air pollution and mortality in Phoenix



### ranging from 0 

### to 4 

### were investigated. We evaluaced the assumption ofalinear relationship using a

### smooth function. This assump· tion was met ifa straight line could be placed within the 

### 95% confidence intervaJs (CIs). 

### Ap-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.

### 5from sam

### ples colieered by the DFP55 (Al, Si, 5 Ca, Fe, Zn, Mn, Pb, 

### Br, K s' OC, and EC). The analysis also included the daily averages of the gaseous species emitted by combustion

sources 

### (CO, N02, 

### and 

### S02)'Factoranalysis is atechnique used [Q explain me corrciations between variables in terms ofunderlying factors thar are not directly measurable. Each factor is a linear combination ofche 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 aset oforthogonaJ variables, we performed a 

### separate regression analysis wim all ofme 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 inJune 

### 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

Table4. 

### 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 

### NO

### z 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. Number4, 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.Swas highly correlated with with cardiovascular mortality with 1 day lag that EC and TCwere significamly associated 

### OC (r= 

### 0.B9), 

### EC (r= 

### 0.B4), 

### TC (r= 

### 0.90), (p < 

### 0.10). Table 7 lists all ofthe 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 OCat 1and 3days lag and TCat 3 days lag. relation coefficients between carbon and a more consisrent associarion with panicwate Ks had a signiflClnt positive association with PM2 . 5indicate that the majority ofthe varia- 

### mass concentrations th<U1. total mortaJity. We cardiovascular mortality (3 day lag). rion in PM2 .5is due to combustion producLS. Table 6. RRfor lotal mortalityin Phoenixfrom an interquanile range (IQR) increase in pollutants. PMCF was correlatedwith 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 1.48x 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 

### 1.15xIOD 

### 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' 

### 1.06x 10D 

### 1.69 0.02 1.04 0.99 1.08 

### Particulate sulfur concentrations peak in the 4 

### 2.20 x100 

### 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 

### 1996because ofme use ofthe WINS inlet. 2 

### ·1.76)( 10.5 6.59 x 10.6 -2.05 1,769.33 0.97 0.94 1.00 

### Summaries ofme RR between the expo- 3 ·1.75 

### x10" 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 

### 1.06x 10.3 

### 5.35

# x10"" 1.98 24.88 1.03 1.00 

### Ins 7, respectively. Because ofspace 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, lower95% confidonce interval; 

### r. t-statistic fromthe regression model; UCI, results in the tables, although models were upper95%conlidenceinterval.
run using all ohhe pollutants listed in Table 5. Tables ofall of the nonsignificant resulLS Table 7. RRfor cardiovascularmortality 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 x10'2 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 1.59x 10° 3.08 0.02 1.10 1.04 1.17 

### eel gaseous air pollutants. CO and 

### N02were 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. Therewas evidence ofa 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 strongassociations 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 morrali- 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 x10,3 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 1and 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 (lagdays 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 1.39x 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 x10.5 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, lower95% con/idenceinterval; t, 

### I'statistic Irornthe rogression model; UCI, 1.02 

### (CI, 1.00--1.05). 

### All PM mass 

### merrics upper95% confidenceinterval.



### 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 ofthe individual pollutant concentrations. 

### The results from the analysis with five facmrs are presented in Table 

### 8. Factor 

### 1 probably represents the influence ofmoror 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 burningwim highloadingson OC and 

### Ks. Factor 

### 4 represents alocal 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 Factor2
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 Percentvariance 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 (lagday 

### 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 ofme chemical components of

### PMCF from samples collected by me dichotomous sampler. Factor 

### 1represents soil with high loadingson

### AI, Si, 

### K, Ca, Mn, Fe, Sr, and Rb. Factor 

### 2 represents a soutce ofcoarse 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% ofthe variance in me

### PMCF dara. 

### Sensitivity 4n4lysis. As a sensitivity analysis, we analyzed temperature as a cofactorramer man aconfounder. 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



### Factor3 Factor4 Factor5

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 Factor2 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 Factor4 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 Factor5 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 Cardiovascularmortality Factor 1 1 0.05 0.02 2.59 1.11 1.06 1.01 1.10 Factor3 3 0.05 0.02 2.67 1.02 1.05 1.01 1.09 Factor5 0 0.04 0.02 2.03 1.38 1.06 1.00 1.12
Abbreviations:**f~,**

### regrllssion coefficient; lCI,lower95% confidence interval; r. ('statistic from the rllgression model; UCI, upper95% 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 

### PM10 (,= 

### -0.08) or PM,., (T=

### -0.25). A 'ecand analysis examined me effect ofextreme temperatures. Ifthe average daily temperature:

was greater than or equal to the 95th percentile 

### (35.4 D

### C), we: assigned a 

### I to me predictorvariable; otherwise we assigned a 

### O. We did not find an association between extreme temperature and total mortality. However, with cardiovascular morrali[)" extreme temperaturewas associated with excess dc:ams at 0 

### and 

### 2days lag (p <

### 0.1). To further assess the imporrance ofme high temperature days to

our analysis, we evaluated- the association between 

### PM2.5 and cardiovascular mortali[), after excluding medays 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 daylag) 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 controllingfor time trends and temperature.

### As a cofactor, rdative humiditywas not associated wich either tOtal mortalityor cardiovascular mortality. To further 

### assess the effectS ofextreme relative humidi[)" we eliminated

me driest days (rdative humidity < 25m percentile) from medata. 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 concurrentday, butnonsignificantwith I-daylag. We also used dew point rather than relative humidityin the base model. Controlling for dew point rather man relative humidity did not alter ourresults. We obtainedsimilar regression coefficients.


### Table 10.Factor analysis results for PMCf'
Element Factor 1 Factor2 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 Percentvariance 51.1 26.5 14.2 explained byfaclor



### 351Articles • Mar et aJ.

To assess the effect ofreplacing the 

### PM

## z5 cyclone on the TEOM with [he WINS,

# we evaluated the association berween soil and rotal mortality from 1 January 1995 ro 31 December 1996and from 1January 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 

# odays lag, bur not significant for any ofthe otherdays. 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 ofthese three approaches. In contrast, total porassium was noc associated wim eithertotal orcardiovascularmonality. 

### Discussion

Toourknowledge 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 ofair 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 PM 10 and cardiovascular mortality 

### is consistent with previous studies. Zmirou
et 

### aI. (J7) reported an RRfor cardiovascular mortality from a 

### 50-llgfm3 increase in PMiD (RR = 1.04) in a study ofair pollution in 10 large Western European cities. Pope et al. 

### (5) found an association between respiratory disease death and cardiovascular deaths with PM10 in Utah. Schwartz (2) also found chat

an high-pollution days {increased tOtal 

### suspended particulates) there was an increased risk ofdeath 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 ofSchwartz et at. (3). who found that a



### 352 10_j.lg/m3increase 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 ofcoarse 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 andmortality. We investigated the possibility that PMCF was a surrogate for dryness by diminating 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 ofsail intrusion into the 

### PM

## z .5sample, which could produce soil data mat are essentiallydifferent 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 ofmorrality. We also evaluated the association between sail and mortality with only soil data obtained with theWINS. 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 ofdays used to evaluate [he association between WINS PM2.5 soil and total morraliry 

### (n· 

### 377). With respect ra rhe elemental components of PMz.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 cardiovascularmonality. 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 mortalityat 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 ofleaded 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 ofthe 

### mass ofPM2 .$ (I5%). The significant negative associations between total mortality and Pb and S were not consistent 

### wich the lackofassociation betwet::n these exposure variables and cardiovascularmonality. 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[cet 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 ofCO and 

### NO

## zare morarvehicles. 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. (J7) 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 offactor analysis in an epidemiologic study. Using facror analysis, we were able to idenci

### fy those underlying factors of measured air pollution composition variability that were associated with excess mortality. Poisson regression with factor scores as exposurevariables revealed that combustion-related pollutants associated with motor vehicles and vegetative burning as well as fine particwate 504 concentrations were significantly associated with cardiovascular monality. The soil facrar. however, was associated wim fewer than expected [otal deaths. These results are

Volume 108, Number4, 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 associaced with cardiovascular monality, whereas S alone: in

an individual pollucanr model was nor associared with cardiovascular mortality. This may be reflective ofthe contribution ofPb and Br

[0 the S factor. A unique: aspecc ofthis study was rhe use of the chemical composition dara ofPM2,S Using such data, we found positive associations berween cardiovascular mortality and 

### K



## s' 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 ofthe 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.

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### 353