Associations between Air Pollution and Mortality in Phoenix, 1995-1997.We evaluated the association between mortality outcomes in elderly individuals and particulate matter particulate matter n. Abbr. PM Material suspended in the air in the form of minute solid particles or liquid droplets, especially when considered as an atmospheric pollutant. Noun 1. (PM) of varying aerodynamic diameters Drug particles for pulmonary delivery are typically characterized by aerodynamic diameter rather than geometric diameter. The velocity at which the drug settles is proportional to the aerodynamic diameter, da. (in micrometers) [[PM.sub.10], [PM.sub.2.5], and [PM.sub.CF] ([PM.sub.10] minus [PM.sub.2.5])], and selected particulate par·tic·u·late adj. Of or occurring in the form of fine particles. n. A particulate substance. particulate composed of separate particles. and gaseous gas·e·ous adj. 1. Of, relating to, or existing as a gas. 2. Full of or containing gas; gassy. phase pollutants pollutants see environmental pollution. in Phoenix, Arizona Phoenix /ˈfiːˌnɪks/ (English: Phoenix, Navajo: Hoozdo, lit. "the place is hot", Western Apache: Fiinigis) is the capital and the most populous city of the U.S. , using 3 years of daily data (1995-1997). Although source apportionment The process by which legislative seats are distributed among units entitled to representation; determination of the number of representatives that a state, county, or other subdivision may send to a legislative body. The U.S. and epidemiologic ep·i·de·mi·ol·o·gy n. The branch of medicine that deals with the study of the causes, distribution, and control of disease in populations. [Medieval Latin epid 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 ar·id adj. 1. Lacking moisture, especially having insufficient rainfall to support trees or woody plants: an arid climate. 2. Southwest and has approximately 1 million residents (9.7% of the residents are [is greater than] 65 years of age). PM data were obtained from the U.S. Environmental Protection Agency Environmental Protection Agency (EPA), independent agency of the U.S. government, with headquarters in Washington, D.C. It was established in 1970 to reduce and control air and water pollution, noise pollution, and radiation and to ensure the safe handling and (EPA EPA eicosapentaenoic acid. EPA abbr. eicosapentaenoic acid EPA, n.pr See acid, eicosapentaenoic. EPA, n. ) National Exposure Research Laboratory Platform in central Phoenix. We obtained gaseous pollutant pol·lut·ant n. Something that pollutes, especially a waste material that contaminates air, soil, or water. data, specifically carbon monoxide carbon monoxide, chemical compound, CO, a colorless, odorless, tasteless, extremely poisonous gas that is less dense than air under ordinary conditions. It is very slightly soluble in water and burns in air with a characteristic blue flame, producing carbon dioxide; , nitrogen dioxide nitrogen dioxide n. A poisonous brown gas, NO2, often found in smog and automobile exhaust fumes and synthesized for use as a nitrating agent, a catalyst, and an oxidizing agent. Noun 1. , ozone, and sulfur dioxide sulfur dioxide, chemical compound, SO2, a colorless gas with a pungent, suffocating odor. It is readily soluble in cold water, sparingly soluble in hot water, and soluble in alcohol, acetic acid, and sulfuric acid. data, from the EPA Aerometric Information Retrieval information retrieval Recovery of information, especially in a database stored in a computer. Two main approaches are matching words in the query against the database index (keyword searching) and traversing the database using hypertext or hypermedia links. System Database. We used Poisson regression In statistics, the Poisson regression model attributes to a response variable Y a Poisson distribution whose expected value depends on a predictor variable x, typically in the following way: adj. weak·li·er, weak·li·est Delicate in constitution; frail or sickly. adv. 1. With little physical strength or force. 2. With little strength of character. associated with [SO.sub.2], [PM.sub.10], and [PM.sub.CF] (p [is less than] 0.10). Cardiovascular mortality was significantly associated with CO, [NO.sub.2], [SO.sub.2], [PM.sub.2.5], [PM.sub.10], [PM.sub.CF] (p [is less than] 0.05), and elemental elemental emanating from or pertaining to elements. elemental diet see elemental diet. carbon. Factor analysis revealed that both combustion-related pollutants and secondary aerosols (sulfates) were associated with cardiovascular mortality. Key words: cardiovascular, composition, factor analysis, particulate matter, [PM.sub.2.5], [PM.sub.10], sources. Environ en·vi·ron tr.v. en·vi·roned, en·vi·ron·ing, en·vi·rons To encircle; surround. See Synonyms at surround. [Middle English envirounen, from Old French environner Health Perspect 108:347-353 (2000). [Online 25 February 2000] http://ehpnet1.niehs.nih.gov/docs/2000/108p347-353mar/abstract.html 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 Any circumstances surrounding the commission of a crime that increase its seriousness or add to its injurious consequences. Such circumstances are not essential elements of the crime but go above and beyond them. of existing respiratory and cardiovascular conditions, altered defense mechanisms, and even premature death Premature Death occurs when a living thing dies of a cause other than old age. A premature death can be the result of injury, illness, violence, suicide, poor nutrition (often stemming from low income), starvation, dehydration, or other factors. . The most susceptible populations include those with preexisting pre·ex·ist or pre-ex·ist v. pre·ex·ist·ed, pre·ex·ist·ing, pre·ex·ists v.tr. To exist before (something); precede: Dinosaurs preexisted humans. v.intr. respiratory or cardiovascular conditions, asthmatics, children, and the elderly (8,11). To date, few epidemiology epidemiology, field of medicine concerned with the study of epidemics, outbreaks of disease that affect large numbers of people. Epidemiologists, using sophisticated statistical analyses, field investigations, and complex laboratory techniques, investigate the cause studies have used PM measures other than size-segregated mass as the exposure metric. Schwartz et al. (12) looked at episodes of high coarse particle concentration in Spokane, Washington Spokane (pronounced [spoʊ̯ˈkæn]) is a city located in Eastern Washington. The seat of Spokane County, Spokane is the metropolitan center of the Inland Northwest, the second largest city in Washington state, and , and found that windblown dust episodes were not associated with increased mortality. In the Harvard Six Cities Study, Schwartz et al. (3) found a significant association between nonaccidental mortality and particulate matter [is less than or equal to] 2.5 [micro]m in aerodynamic diameter ([PM.sub.2.5]) and sulfur. They did not find a significant association with particulate matter [is less than or equal to] 10 [micro]m in aerodynamic diameter ([PM.sub.10]) or the coarse fraction of PM [[PM.sub.CF] ([PM.sub.10] minus [PM.sub.2.5])]. In contrast, Ostro et al. (13) found that [PM.sub.10] dominated by coarse particles was associated with an increase in mortality in the Coachella Valley Coachella Valley (kō'əchĕl`ə), arid region, SE Calif., N of the Salton Sea. Water is brought into the region by artesian wells and by the Coachella Canal (123 mi/198 km long), a branch of the All-American Canal built between 1938 and in California. The differences in the results from these two studies may be due to the particulate composition as well as the difference in the amount of [PM.sub.CF]. In the eastern United States United States, officially United States of America, republic (2005 est. pop. 295,734,000), 3,539,227 sq mi (9,166,598 sq km), North America. The United States is the world's third largest country in population and the fourth largest country in area. , [PM.sub.2.5] is dominated by sulfates (34%), whereas in the western and central United States The Central United States is sometimes conceived as between the Eastern United States and Western United States as part of a three-region model, roughly coincident with the Midwestern United States plus the western and central portions of the Southern United States; the term is it is dominated by organic carbon (OC) from motor vehicles and vegetative vegetative /veg·e·ta·tive/ (vej?e-ta?tiv) 1. of, pertaining to, or characteristic of plants. 2. concerned with growth and nutrition, as opposed to reproduction. 3. burning (39%) (14). The average [PM.sub.2.5]/[PM.sub.10] ratio 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 to evaluate the associations between daily air pollution and total nonaccidental and cardiovascular mortality in Phoenix. Phoenix is an arid southwestern city with a population of approximately 1 million residents (16). It is an interesting location because of its large proportion of elderly people (9.7% of the population is [is greater than] 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 pave tr.v. paved, pav·ing, paves 1. To cover with a pavement. 2. To cover uniformly, as if with pavement. 3. To be or compose the pavement of. road dust, and vegetative burning (15). This study focused on the effects of air pollution on cardiovascular mortality for several reasons. First, the association between air pollutants and cardiovascular mortality has been consistent in previous studies (1,2,9,17). Second, a study in Baltimore, Maryland "Baltimore" redirects here. For the surrounding county, see Baltimore County, Maryland. For other uses, see Baltimore (disambiguation). Baltimore is an independent city located in the state of Maryland in the United States. , found that heart rate variability Heart rate variability (HRV) is a measure of variations in the heart rate. It is usually calculated by analysing the time series of beat-to-beat intervals from ECG or arterial pressure tracings. was associated with [PM.sub.2.5] in elderly subjects with cardiovascular conditions (18). Finally, in this study cardiovascular mortality had the largest sample size, accounting for 45% of the total non accidental deaths in the study region (based on zip codes zip code System of postal-zone codes (zip stands for “zone improvement plan”) introduced in the U.S. in 1963 to improve mail delivery and exploit electronic reading and sorting capabilities. ). This may be reflective of the increased size of Phoenix's elderly population, which is more prone to cardiovascular disease Cardiovascular disease Disease that affects the heart and blood vessels. Mentioned in: Lipoproteins Test cardiovascular disease . A unique aspect of this study is that our pollution data include daily information not only on traditional gaseous pollutants, but also on PM in various size fractions and the chemical composition of [PM.sub.2.5]. From 1995 to 1997, the U.S. Environmental Protection Agency (EPA) National Exposure Research Laboratory (NERL NERL National Exposure Research Laboratory ) operated a comprehensive monitoring platform in Phoenix. They collected daily [PM.sub.2.5] samples and subsequently analyzed an·a·lyze tr.v. an·a·lyzed, an·a·lyz·ing, an·a·lyz·es 1. To examine methodically by separating into parts and studying their interrelations. 2. Chemistry To make a chemical analysis of. 3. them for various chemical components of PM. This provided an opportunity to examine more specific metrics metrics Managed care A popular term for standards by which the quality of a product, service, or outcome of a particular form of Pt management is evaluated. See TQM. for PM than simply mass, as well as an opportunity to identify selected chemical components of PM that are associated with mortality. In addition to PM, this study also evaluated the association between total nonaccidental and cardiovascular mortality and other measured air pollutants: carbon monoxide, nitrogen dioxide, sulfur dioxide, and ozone. These EPA criteria pollutants are also associated with mortality (7,17,19,20). Methods Study area and data. Mortality data for all of Maricopa County from 1995 to 1997 were obtained from the Arizona Center Arizona Center is a shopping center and office complex located in downtown Phoenix, Arizona. Arizona Center was designed by the Rouse Company (on its festival marketplace model, which worked to great success in other cities) and opened in the fall of 1990 to great fanfare for Health Statistics in Phoenix. Death certificate data included residence zip code and the primary cause of death as identified by the International Classification of Diseases, Ninth Revision (ICD-9, World Health Organization, Geneva Geneva, canton and city, Switzerland Geneva (jənē`və), Fr. Genève, canton (1990 pop. 373,019), 109 sq mi (282 sq km), SW Switzerland, surrounding the southwest tip of the Lake of 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 the Arizona Department of Environmental Quality (Phoenix, AZ). We evaluated total nonaccidental mortality (ICD-9 codes The following is a list of codes for International Statistical Classification of Diseases and Related Health Problems. These codes are in the public domain. Table 1. Mortality counts for individuals [is greater than or equal to] 65 years of age in Phoenix.
Total Average nonaccidental
Year nonaccidental deaths/day
1995 3,072 8.45
1996 3,201 8.74
1997 3,003 8.45
1995-1997 9,276 8.55
Total Average cardiovascular
Year cardiovascular deaths/day
1995 1,391 3.86
1996 1,473 3.98
1997 1,318 3.73
1995-1997 4,182 3.85
We obtained [PM.sub.2.5], [PM.sub.10], [PM.sub.CF], and [PM.sub.2.5] chemical composition data from the EPA NERL platform in central Phoenix. Chemical composition was only available for [PM.sub.2.5]. The monitoring platform is approximately 10 km west-northwest of downtown Phoenix The downtown of the city of Phoenix in the U.S. state of Arizona covers about two or three square miles, with axes running along Central Avenue and Washington/Jefferson Streets. About twenty-five mid-rise and high-rise buildings ranging up to 39 stories tall pierce the skyline. at a state and local air monitoring station. Standard meteorologic me·te·or·ol·o·gy n. The science that deals with the phenomena of the atmosphere, especially weather and weather conditions. [French météorologie, from Greek parameters such as wind speed and direction, temperature, and relative humidity relative humidity n. The ratio of the amount of water vapor in the air at a specific temperature to the maximum amount that the air could hold at that temperature, expressed as a percentage. were continuously measured. The average temperature in Phoenix from 1995 to 1997 was 23.7 [+ or -] 8.1 [degrees] C. The average relative humidity was 32 [+ or -] 15%. NERL investigators made hourly [PM.sub.2.5] and [PM.sub.10] measurements each day using two colocated tapered ta·per n. 1. A small or very slender candle. 2. A long wax-coated wick used to light candles or gas lamps. 3. A source of feeble light. 4. a. element oscillation Oscillation Any effect that varies in a back-and-forth or reciprocating manner. Examples of oscillation include the variations of pressure in a sound wave and the fluctuations in a mathematical function whose value repeatedly alternates above and below some microbalance mi·cro·bal·ance n. A balance designed to weigh very small loads, up to 0.1 gram. Noun 1. microbalance - balance for weighing very small objects balance - a scale for weighing; depends on pull of gravity (TEOM TEOM Tapered Element Oscillating Microbalance ) monitors (Rupprecht & Patasnick Co., Albany, NY). The TEOM-[PM.sub.10] was fitted with an EPA-approved federal reference method [PM.sub.10] impactor inlet inlet /in·let/ (-let) a means or route of entrance. pelvic inlet the upper limit of the pelvic cavity. thoracic inlet the elliptical opening at the summit of the thorax. (model 246b; Andersen Instruments, Smyrna, GA). The TEOM-[PM.sub.2.5] was fitted with a [PM.sub.2.5] cyclone cyclone, atmospheric pressure distribution in which there is a low central pressure relative to the surrounding pressure. The resulting pressure gradient, combined with the Coriolis effect, causes air to circulate about the core of lowest pressure in a inlet (University Research Glassware, Chapel Hill, NC). The [PM.sub.2.5] cyclone on the TEOM was replaced with a well-impactor ninety-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 [PM.sub.CF]) as TEOM [PM.sub.10] minus TEOM [PM.sub.2.5]. NERL investigators collected the daily gravimetric gravimetric /grav·i·met·ric/ (grav?i-me´trik) pertaining to measurement by weight; performed by weight, as a gravimetric method of drug assay. grav·i·met·ric adj. 1. integrated 24-hr (starting at 0700) fine particle filter This article is about the statistical method. For the pollution control device, see diesel particulate filter. Particle filters, also known as Sequential Monte Carlo methods (SMC), are sophisticated model estimation techniques based on simulation. samples using a dual fine particle sequential sampler sampler, sample piece of needlework or embroidery, of silk, cotton, or worsted, for the preservation of some pattern or as an example of the ability of a child or a beginner. In museums and private collections there are samplers dating from as early as 1643. (DFPSS; University Research Glassware). The DFPSS was fitted with a cyclone that was identical to the cyclone on the TEOM-[PM.sub.2.5]. The DFPSS collected daily samples on both Teflon and quartz filters. The Teflon filter was used for mass and elemental analysis Elemental analysis is a process where a sample of some material (e.g., soil, waste or drinking water, bodily fluids, minerals, chemical compounds) is analyzed for its elemental and sometimes isotopic composition. , whereas the quartz filter was used for carbon analysis. In addition to the DFPSS, NERL investigators operated a dichotomous di·chot·o·mous adj. 1. Divided or dividing into two parts or classifications. 2. Characterized by dichotomy. di·chot sampler (Andersen Instruments, Inc.) every third day beginning 17 June 1996. Both the [PM.sub.2.5] and [PM.sub.CF] samples were collected on Teflon filters. The investigators measured elemental concentrations at the EPA (Research Triangle Park Research Triangle Park, research, business, medical, and educational complex situated in central North Carolina. It has an area of 6,900 acres (2,795 hectares) and is 8 × 2 mi (13 × 3 km) in size. Named for the triangle formed by Duke Univ. , NC) with energy dispersive dispersive /dis·per·sive/ (-per´siv) 1. tending to become dispersed. 2. promoting dispersion. X-ray fluorescence X-ray fluorescence (XRF) is the emission of characteristic "secondary" (or fluorescent) X-rays from a material that has been excited by bombarding with high-energy X-rays or gamma rays. . OC and elemental carbon (EC) were measured by Sunset Laboratory (Forest Grove, OR) using thermal optical transmittance (21). PM and gaseous pollutant concentrations (range and mean [+ or -] SD) from 1995 to 1997 are presented in Table 2. We obtained gaseous criteria pollutant data for CO, [NO.sub.2], [O.sub.3], and [SO.sub.2] 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 [NO.sub.2] over two sites. Only one residential monitoring site was available for [SO.sub.2]. We averaged the hourly averages for CO, [NO.sub.2], and [SO.sub.2] over 24 hr from 0700 to 0700. We used the maximum hourly [O.sub.3] ([O.sub.3] max) concentration in the same 24-hr period in the analysis. Table 2. Annual range of pollutant concentrations (1995-1997). Particulate matter pollutant, year Range Gaseous pollutant [PM.sub.2.5] (DFPSS) CO (ppm) 1995 4-37 1996 3-39 1997 2-35 3-year mean 12.0 [+ or -] 6.6 [PM.sub.10] (TEOM) [NO.sub.2] (ppb) 1995 9-129 1996 5-213 1997 7-186 3-year mean 46.5 [+ or -] 22.3 [PM.sub.2.5] (TEOM) [O.sub.3] max (ppb) 1995 1-40 1996 0-42 1997 1-34 3-year mean 13.0 [+ or -] 7.2 [PM.sub.CF] (TEOM) [SO.sub.2] (ppb) 1995 5-104 1996 5-187 1997 5-159 3-year mean 33.5 [+ or -] 17.3 Particulate matter pollutant, year Range [PM.sub.2.5] (DFPSS) 1995 0.5-4.0 1996 0.3-4.0 1997 0.3-3.7 3-year mean 1.5 [+ or -] 08 [PM.sub.10] (TEOM) 1995 8-64 1996 9-59 1997 8-61 3-year mean 30 [+ or -] 10 [PM.sub.2.5] (TEOM) 1995 10-131 1996 14-112 1997 14-104 3-year mean 57.0 [+ or -] 17.7 [PM.sub.CF] (TEOM) 1995 0-11 1996 1-17 1997 2-12 3-year mean 3.1 [+ or -] 2.2 The [PM.sub.2.5] constituents that we evaluated for effects on mortality were sulfur, zinc, lead, soil-corrected potassium potassium (pətăs`ēəm), a metallic chemical element; symbol K [Lat. kalium=alkali]; at. no. 19; at. wt. 39.0983; m.p. 63.25°C;; b.p. 760°C;; sp. gr. .862 at 20°C;; valence +1. ([K.sub.S]) (23), OC, EC, total carbon (TC), and reconstructed re·con·struct tr.v. re·con·struct·ed, re·con·struct·ing, re·con·structs 1. To construct again; rebuild. 2. soil. Soil was reconstructed by summing the oxides of Al, Si, Ca, Fe, and Ti using the formula recommended by Maim et al. (24). We also considered [PM.sub.2.5] that was corrected for soil content (nonsoil [PM.sub.2.5] = [PM.sub.2.5] - reconstructed soil). Table 3 presents the percent of the total mass of [PM.sub.2.5] accounted for by each component. The elements aluminum, silicon, calcium, titanium titanium (tītā`nēəm, tĭ–) [from Titan], metallic chemical element; symbol Ti; at. no. 22; at. wt. 47.88; m.p. 1,675°C;; b.p. 3,260°C;; sp. gr. 4.54 at 20°C;; valence +2, +3, or +4. , and iron were not evaluated separately in the mortality analysis because they are the major elemental components of soil. Table 3. Percent of total mass of [PM.sub.2.5] accounted for by each component. Component [PM.sub.2.5] (%) S(a) 3.69 Mn 0.05 Zn 0.15 Br 0.03 Pb 0.06 OC(a) 1.4 38.37 EC 10.78 [K.sub.S] 0.52 Soil(b) 17.50 (a) If S is assumed to be in the form of [([NH.sub.4]).sub.2][SO.sub.4], the mass percent would be 15.2%. (b) 2.20% AI + 2.49% Si + 1.63% Ca + 2.42% Fe + 1.94% Ti (23). Statistical analysis. In our zip-code regions, we analyzed a total of 9,276 nonaccidental deaths from 1995 to 1997. Poisson regression was used to evaluate the association between the air pollutant exposure variables and the mortality outcomes (2,5). We used Poisson regression because mortality 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 overdispersion parameter was 1.05 and 1.00 for nonaccidental and cardiovascular mortality, respectively. We calculated all relative risks (RRs) for an interquartile increase (25th-75th percentile percentile, n the number in a frequency distribution below which a certain percentage of fees will fall. E.g., the ninetieth percentile is the number that divides the distribution of fees into the lower 90% and the upper 10%, or that fee level ) in pollutant concentration. The effect of air pollution on mortality is small and can be influenced by confounders. Therefore, base models for total mortality and cardiovascular mortality were constructed by adjusting for day of the week with indicator variables, and time trends, temperature, and relative humidity with smoothing functions (25). We determined degrees of freedom (df) for the function used to smooth time trend by minimizing autocorrelation Autocorrelation The correlation of a variable with itself over successive time intervals. Sometimes called serial correlation. as well as the Akaike information criterion Akaike's information criterion, developed by Hirotsugu Akaike under the name of "an information criterion" (AIC) in 1971 and proposed in Akaike (1974), is a measure of the goodness of fit of an estimated statistical model. It is grounded in the concept of entropy. (AIC AIC Association des Infermières Canadiennes. ) (26). We chose the df and lag for the smoothing functions for temperature and relative humidity to minimize the AIC. The base model for total mortality used indicator variables for day of the week, 10 df for time trends, 2 df for temperature with 2 days lag, and 2 df for relative humidity with 0 days lag. The base model for cardiovascular mortality used indicator variables for the day of the week, 10 df for time trends, 2 df for temperature with 1 day lag, and 2 df for relative humidity with 0 days lag. We included continuous daily data from 1995 to 1997 (1,097 days) in the study. Each day was coded and included in the model to adjust for time trends. Little autocorrelation was observed after adjusting for day of week, time trends, temperature, and relative humidity. The autocorrelation for days 1-25 for both total and cardiovascular mortality were within the 95% confidence interval confidence interval, n a statistical device used to determine the range within which an acceptable datum would fall. Confidence intervals are usually expressed in percentages, typically 95% or 99%. 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 metrics that were evaluated in this analysis included CO, [NO.sub.2], [O.sub.3], [SO.sub.2], TEOM [PM.sub.10], TEOM [PM.sub.2.5], TEOM [PM.sub.CF], [PM.sub.2.5] (DFPSS), S, Zn, Pb, soil, [K.sub.S], nonsoil PM, OC, EC, and TC. Lag days ranging from 0 to 4 were investigated. We evaluated the assumption of a linear relationship using a smooth function. This assumption was met if a straight line could be placed within the 95% confidence intervals (CIs). A p-value [is less than] 0.05 associated with the pollution exposure variable was considered significant. We conducted Poisson regression analyses using S-PLUS 4 (Mathsoft, Inc., Seattle, WA) Factor analysis. We conducted a factor analysis on the daily concentrations of the chemical components of [PM.sub.2.5] from samples collected by the DFPSS (Al, Si, S Ca, Fe, Zn, Mn, Pb, Br, [K.sub.S], OC, and EC). The analysis also included the daily averages of the gaseous species emitted by combustion sources (CO, [NO.sub.2], and [SO.sub.2]). Factor analysis is a technique used to explain the correlations between variables in terms of underlying factors that are nor directly measurable. Each factor is a linear combination of the original variables and all such factors are orthogonal At right angles. The term is used to describe electronic signals that appear at 90 degree angles to each other. It is also widely used to describe conditions that are contradictory, or opposite, rather than in parallel or in sync with each other. to each other. The factors were extracted using principal component analysis with a varimax rotation. We conducted factor analysis using SAS (1) (SAS Institute Inc., Cary, NC, www.sas.com) A software company that specializes in data warehousing and decision support software based on the SAS System. Founded in 1976, SAS is one of the world's largest privately held software companies. See SAS System. (SAS Institute SAS Institute Inc., headquartered in Cary, North Carolina, USA, has been a major producer of software since it was founded in 1976 by Anthony Barr, James Goodnight, John Sall and Jane Helwig. Inc, Cary, NC). We used the resultant factor scores as surrogate surrogate n. 1) a person acting on behalf of another or a substitute, including a woman who gives birth to a baby of a mother who is unable to carry the child. 2) a judge in some states (notably New York) responsible only for probates, estates, and adoptions. exposure variables in predicting mortality outcomes with the Poisson regression model. Each factor was evaluated in a single source model. However, because the factor scores formed a set of orthogonal variables, we performed a separate regression analysis In statistics, a mathematical method of modeling the relationships among three or more variables. It is used to predict the value of one variable given the values of the others. For example, a model might estimate sales based on age and gender. with all of the scores included in one multifactor model. We also conducted a factor analysis on the daily concentrations of the chemical components of [PM.sub.CF] from samples collected by the dicot (Al, Si, Cl, S, K Ca, Mn, Fe, Zn, Br, Pb, Sr, Cu, and Rb). We did not use the scores from this analysis in the time-series analysis Time-series analysis Assessment of relationships between two or among more variables over periods of time. because the sampling period started in June 1996 and samples were only collected every third day. Results Table 4 shows the correlation coefficients Correlation Coefficient A measure that determines the degree to which two variable's movements are associated. The correlation coefficient is calculated as: between PM, gaseous pollutants, temperature, and relative humidity for Phoenix in 1995-1997. [PM.sub.2.5] (obtained from the DFPSS) was highly correlated cor·re·late v. cor·re·lat·ed, cor·re·lat·ing, cor·re·lates v.tr. 1. To put or bring into causal, complementary, parallel, or reciprocal relation. 2. with CO (r = 0.85) and [NO.sub.2] (r = 0.79), but less so with [SO.sub.2] (r =0.43). [PM.sub.2.5] from the DFPSS was highly correlated with that measured with the TEOM (r = 0.93). Table 5 shows the correlation coefficients between selected chemical composition components of [PM.sub.2.5] and the other air pollutants. TEOM [PM.sub.10] was correlated with fine soil (r = 0.72), OC (r = 0.58), EC (r = 0.58), and TC (r = 0.59). TEOM [PM.sub.2.5] was highly correlated with OC (r = 0.89), EC (r = 0.84), TC (r = 0.90), and to a lesser extent with Zn (r = 0.61), Pb (r = 0.67), and [K.sub.S] (r = 0.59). The high correlation coefficients between carbon and [PM.sub.2.5] indicate that the majority of the variation in [PM.sub.2.5] is due to combustion products. [PM.sub.CF] was correlated with soil (r = 0.66). Table 4. Correlation coefficients between PM, gaseous pollutants, temperature, and relative humidity (RH) for Phoenix, 1995-1997.
[PM.sub.10]
[PM.sub.2.5](a) Temp (TEOM)
[PM.sub.2.5](a) 1.00 -0.31 0.69
Temp - 1.00 -0.08
[PM.sub.10] (TEOM) - - 1.00
RH - - -
[PM.sub.2.5] (TEOM) - - -
[PM.sub.CF] (TEOM) - - -
CO - - -
[NO.sub.2] - - -
[O.sub.3] - - -
[SO.sub.2] - - -
[PM.sub.2.5] [PM.sub.CF]
RH (TEOM) (TEOM) CO
[PM.sub.2.5](a) 0.16 0.93 0.50 0.85
Temp -0.55 -0.25 0.00 -0.49
[PM.sub.10] (TEOM) -0.12 0.77 0.97 0.53
RH 1.00 0.09 -0.19 0.23
[PM.sub.2.5] (TEOM) - 1.00 0.59 0.82
[PM.sub.CF] (TEOM) - - 1.00 0.34
CO - - - 1.00
[NO.sub.2] - - - -
[O.sub.3] - - - -
[SO.sub.2] - - - -
[NO.sub.2] [O.sub.3max] [SO.sub.2]
[PM.sub.2.5](a) 0.79 -0.24 0.43
Temp -0.40 0.71 -0.38
[PM.sub.10] (TEOM) 0.53 -0.12 0.41
RH 0.08 -0.54 0.10
[PM.sub.2.5] (TEOM) 0.77 -0.20 0.48
[PM.sub.CF] (TEOM) 0.37 -0.08 0.33
CO 0.87 -0.40 0.53
[NO.sub.2] 1.00 -0.24 0.57
[O.sub.3] - 1.00 -0.37
[SO.sub.2] - - 1.00
(a) Measured with the DFPSS.
Table 5. Correlation coefficient matrix of air pollutants.
S Zn Pb OC EC TC
S 1.00 0.14 0.25 0.12 0.04 0.10
Zn - 1.00 0.63 0.62 0.71 0.65
Pb - - 1.00 0.69 0.69 0.71
OC - - - 1.00 0.91 0.99
EC - - - - 1.00 0.95
TC - - - - - 1.00
[K.sub.S] - - - - - -
[PM.sub.10](a) - - - - - -
[PM.sub.2.5](a) - - - - - -
[PM.sub.CF](a) - - - - - -
Nonsoil [PM.sub.2.5] - - - - - -
Soil - - - - - -
CO - - - - - -
[NO.sub.2] - - - - - -
[O.sub.3] - - - - - -
[O.sub.3] max - - - - - -
[SO.sub.2] - - - - - -
[K.sub.S] [PM.sub.10] [PM.sub.2.5]
S 0.02 0.19 0.27
Zn 0.30 0.46 0.61
Pb 0.39 0.48 0.67
OC 0.65 0.58 0.89
EC 0.57 0.58 0.84
TC 0.64 0.59 0.90
[K.sub.S] 1.00 0.34 0.59
[PM.sub.10](a) - 1.00 0.79
[PM.sub.2.5](a) - - 1.00
[PM.sub.CF](a) - - -
Nonsoil [PM.sub.2.5] - - -
Soil - - -
CO - - -
[NO.sub.2] - - -
[O.sub.3] - - -
[O.sub.3] max - - -
[SO.sub.2] - - -
Nonsoil
[PM.sub.CF] [PM.sub.2.5] Soil CO
S 0.13 0.26 0.25 0.01
Zn 0.33 0.63 0.49 0.65
Pb 0.34 0.71 0.49 0.71
OC 0.38 0.96 0.52 0.89
EC 0.40 0.89 0.52 0.90
TC 0.39 0.96 0.53 0.91
[K.sub.S] 0.19 0.64 0.26 0.52
[PM.sub.10](a) 0.97 0.62 0.72 0.55
[PM.sub.2.5](a) 0.60 0.91 0.64 0.82
[PM.sub.CF](a) 1.00 0.41 0.66 0.37
Nonsoil [PM.sub.2.5] - 1.00 0.54 0.87
Soil - - 1.00 0.48
CO - - - 1.00
[NO.sub.2] - - - -
[O.sub.3] - - - -
[O.sub.3] max - - - -
[SO.sub.2] - - - -
[NO.sub.2] [O.sub.3] [O.sub.3max]
S 0.04 0.13 0.31
Zn 0.62 -0.49 -0.27
Pb 0.63 -0.51 -0.30
OC 0.81 -0.57 -0.32
EC 0.82 -0.64 -0.41
TC 0.83 -0.60 -0.35
[K.sub.S] 0.45 -0.27 -0.14
[PM.sub.10](a) 0.56 -0.25 -0.11
[PM.sub.2.5](a) 0.77 -0.44 -0.19
[PM.sub.CF](a) 0.39 -0.14 -0.07
Nonsoil [PM.sub.2.5] 0.80 -0.54 -0.29
Soil 0.49 -0.17 0.05
CO 0.87 -0.68 -0.39
[NO.sub.2] 1.00 -0.60 -0.24
[O.sub.3] - 1.00 0.81
[O.sub.3] max - - 1.00
[SO.sub.2] - - -
[SO.sub.2]
S -0.07
Zn 0.26
Pb 0.33
OC 0.49
EC 0.46
TC 0.49
[K.sub.S] 0.25
[PM.sub.10](a) 0.42
[PM.sub.2.5](a) 0.47
[PM.sub.CF](a) 0.35
Nonsoil [PM.sub.2.5] 0.46
Soil 0.09
CO 0.51
[NO.sub.2] 0.56
[O.sub.3] -0.46
[O.sub.3] max -0.37
[SO.sub.2] 1.00
(a) Based on TEOM measurements. OC and EC concentrations follow a seasonal pattern--they are high in the colder months and low in the warmer months. This pattern is due to increased com-bustion emissions from space heating Space heating is the heating of a space, usually enclosed, such as a house or room. A space heater keeps the air and surroundings at a comfortable temperature for people or animals, or even plants in a greenhouse. and the decreased mixing height during the winter months. Particulate sulfur concentrations peak in the warmer months. Soil concentration also follows a seasonal trend, with higher concentrations in the spring and fall. Measured soil concentrations decreased after 20 December 1996 because of the use of the WINS inlet. Summaries of the RR between the exposure variables and both total and cardiovascular mortality are presented in Tables 6 and 7, respectively. Because of space limitations, we only present statistically significant (p [is less than] 0.05) and marginally significant (p [is less than] 0.10) results in the tables, although models were run using all of the pollutants listed in Table 5. Tables of all of the nonsignificant non·sig·nif·i·cant adj. 1. Not significant. 2. Having, producing, or being a value obtained from a statistical test that lies within the limits for being of random occurrence. results are available from the authors by request. We evaluated the associations between total and cardiovascular mortality and the gaseous pollutants, PM mass metrics, and PM composition metrics using single-pollutant models. Table 6. RR for total mortality in Phoenix from an interquartile range In descriptive statistics, the interquartile range (IQR), also called the midspread, middle fifty and middle of the #s, is a measure of statistical dispersion, being equal to the difference between the third and first quartiles. (IQR IQR Interquartile Range (statistics) IQR Internet Quick Reference IQR Individual Qualification Record IQR Internal Quality Review ) increase in pollutants.
Pollutant Lag days [Beta]
CO 0 4.50 x [10.sup.-2]
1 4.15 x [10.sup.-2]
[NO.sub.2] 0 2.64 x [10.sup.0]
1 3.29 x [10.sup.0]
3 1.80 x [10.sup.0]
4 2.20 x [10.sup.0]
[SO.sub.2] 0 1.17 x [10.sup.-2]
S 3 -1.38 x [10.sup.-4]
4 -1.10 x [10.sup.-4]
Soil 1 -1.75 x [10.sup.-5]
2 -1.76 x [10.sup.-5]
3 -1.75 x [10.sup.-5]
4 -1.47 x [10.sup.-5]
[PM.sub.10] (TEOM) 0 1.06 x [10.sup.-3]
[PM.sub.CF] (TEOM) 0 1.17 x [10.sup.-3]
Pb 3 -2.70 x [10.sup.-3]
Pollutant Lag days SE t
CO 0 1.48 x [10.sup.-2] 3.05
1 1.48 x [10.sup.-2] 2.81
[NO.sub.2] 0 1.15 x [10.sup.0] 2.31
1 1.13 x [10.sup.0] 2.91
3 1.06 x [10.sup.0] 1.69
4 1.07 x [10.sup.0] 2.05
[SO.sub.2] 0 6.37 x [10.sup.-3] 1.84
S 3 6.24 x [10.sup.-5] -2.21
4 6.10 x [10.sup.-5] -1.80
Soil 1 8.67 x [10.sup.-6] -2.01
2 8.59 x [10.sup.-6] -2.05
3 8.56 x [10.sup.-6] -2.04
4 8.54 x [10.sup.-6] -1.72
[PM.sub.10] (TEOM) 0 5.35 x [10.sup.-4] 1.98
[PM.sub.CF] (TEOM) 0 6.99 x [10.sup.-4] 1.68
Pb 3 1.59 x [10.sup.-3] -1.69
Pollutant Lag days IQR RR LCI UCI
CO 0 1.19 1.06 1.02 1.09
1 1.19 1.05 1.01 1.09
[NO.sub.2] 0 0.02 1.05 1.01 1.10
1 0.02 1.07 1.02 1.12
3 0.02 1.04 0.99 1.08
4 0.02 1.04 1.00 1.09
[SO.sub.2] 0 2.78 1.03 1.00 1.07
S 3 280.60 0.96 0.93 1.00
4 279.90 0.97 0.94 1.00
Soil 1 1,767.45 0.97 0.94 1.00
2 1,769.33 0.97 0.94 1.00
3 1,772.48 0.97 0.94 1.00
4 1,775.62 0.97 0.95 1.00
[PM.sub.10] (TEOM) 0 24.88 1.03 1.00 1.05
[PM.sub.CF] (TEOM) 0 18.39 1.02 1.00 1.05
Pb 3 6.00 0.98 0.97 1.00
Abbreviations: [Beta], regression coefficient Regression coefficient Term yielded by regression analysis that indicates the sensitivity of the dependent variable to a particular independent variable. See: Parameter. regression coefficient ; LCI LCI Livable Centers Initiative LCI Life Cycle Inventory LCI Landing Craft, Infantry LCI La Chaine Info (French cable news channel) LCI Lean Construction Institute LCI Lions Club International , lower 95% confidence interval; t, t-statistic from the regression model; UCI UCI University of California, Irvine UCI Union Cycliste Internationale (International Cycling Union) UCI Unidad de Cuidados Intensivos UCI United Cinemas International (UK) , upper 95% confidence interval. Table 7. RR for cardiovascular mortality from an interquartile range (IQR) increase in pollutants.
Lag
Pollutant days [Beta] SE
CO 0 4.49 x [10.sup.-2] 2.14 x [10.sup.-2]
1 7.66 x [10.sup.-2] 2.07 x [10.sup.-2]
2 5.79 x [10.sup.-2] 2.00 x [10.sup.-2]
3 5.32 x [10.sup.-2] 2.03 x [10.sup.-2]
4 6.43 x [10.sup.-2] 2.06 x [10.sup.-2]
[NO.sub.2] 1 4.88 x [10.sup.0] 1.59 x [10.sup.0]
2 2.53 x [10.sup.0] 1.54 x [10.sup.0]
3 2.76 x [10.sup.0] 1.55 x [10.sup.0]
4 5.74 x [10.sup.0] 1.57 x [10.sup.0]
[SO.sub.2] 2 1.63 x [10.sup.-2] 8.64 x [10.sup.-3]
3 1.85 x [10.sup.-2] 8.65 x [10.sup.-3]
4 2.49 x [10.sup.-2] 8.58 x [10.sup.-3]
[K.sub.S] 3 5.81 x [10.sup.-4] 2.96 x [10.sup.-4]
[PM.sub.10] 0 1.88 x [10.sup.-3] 7.66 x [10.sup.-4]
(TEOM) 1 1.47 x [10.sup.-3] 7.56 x [10.sup.-4]
[PM.sub.2.5] 0 3.91 x [10.sup.-3] 2.38 x [10.sup.-3]
(TEOM) 1 6.85 x [10.sup.-3] 2.36 x [10.sup.-3]
3 4.86 x [10.sup.-3] 2.35 x [10.sup.-3]
4 5.43 x [10.sup.-3] 2.35 x [10.sup.-3]
[PM.sub.CF] 0 2.50 x [10.sup.-3] 9.88 x [10.sup.-4]
1 1.62 x [10.sup.-3] 9.78 x [10.sup.-4]
Nonsoil 1 5.56 x [10.sup.-6] 3.12 x [10.sup.-6]
[PM.sub.2.5]
OC 1 1.46 x [10.sup.-5] 6.82 x [10.sup.-6]
3 1.39 x [10.sup.-5] 6.89 x [10.sup.-6]
EC 1 4.40 x [10.sup.-5] 1.82 x [10.sup.-5]
TC 1 1.15 x [10.sup.-5] 5.05 x [10.sup.-6]
3 9.71 x [10.sup.-6] 5.10 x [10.sup.-6]
Pollutant t IQR RR LCI UCI
CO 2.10 1.19 1.05 1.00 1.11
3.71 1.19 1.10 1.04 1.15
2.89 1.19 1.07 1.02 1.12
2.63 1.19 1.07 1.02 1.12
3.12 1.19 1.08 1.03 1.13
[NO.sub.2] 3.08 0.02 1.10 1.04 1.17
1.64 0.02 1.05 0.99 1.12
1.78 0.02 1.06 0.99 1.12
3.66 0.02 1.12 1.05 1.19
[SO.sub.2] 1.88 2.78 1.05 1.00 1.10
2.14 2.79 1.05 1.00 1.10
2.90 2.79 1.07 1.02 1.12
[K.sub.S] 1.97 55.62 1.03 1.00 1.07
[PM.sub.10] 2.46 24.88 1.05 1.01 1.09
(TEOM) 1.95 24.88 1.04 1.00 1.08
[PM.sub.2.5] 1.64 8.52 1.03 0.99 1.08
(TEOM) 2.90 8.52 1.06 1.02 1.10
2.07 8.51 1.04 1.00 1.08
2.31 8.47 1.05 1.01 1.09
[PM.sub.CF] 2.54 18.39 1.05 1.01 1.09
1.66 18.39 1.03 0.99 1.07
Nonsoil 1.78 6,601.06 1.04 1.00 1.08
[PM.sub.2.5]
OC 2.15 2,976.50 1.04 1.00 1.09
2.02 2,960.00 1.04 1.00 1.08
EC 2.42 1,165.50 1.05 1.01 1.10
TC 2.28 4,169.00 1.05 1.01 1.09
1.90 4,170.00 1.04 1.00 1.09
Abbreviations: [Beta], regression coefficient; LCI, lower 95% confidence interval; t, t-statistic from the regression model; UCI, upper 95% confidence interval. We found significant associations between both mortality outcomes and selected gaseous air pollutants. CO and [NO.sub.2] were positively associated with total mortality at 0- and 1-day lags. There was evidence of a weak association with [SO.sub.2] at 0 days lag (p [is less than] 0.10). We found several strong associations with cardiovascular mortality. Cardiovascular mortality was positively associated with CO (0-4 days lag). This was the most consistent association because the association was significant for all 5 lag days. Statistically significant associations (p [is less than] 0.05) were also evident with [NO.sub.2] on lag days 1 and 4, although the association was weaker on lag days 2 and 3. Cardiovascular mortality was also associated with [SO.sub.2] (lag days 2, 3, and 4). We also found significant associations between the mortality outcomes and particulate mass. The associations between [PM.sub.10] and total mortality, and between [PM.sub.CF] and total mortality, were marginal (p [is less than] 0.10). Total mortality was not significantly associated with [PM.sub.2.5]; however, the RR was 1.02 (CI, 1.00-1.05). All PM mass metrics were associated with an excess risk of cardiovascular death. The strongest associations were with [PM.sub.2.5] (TEOM), followed by [PM.sub.10] and [PM.sub.CF]. [PM.sub.2.5] adjusted for soil content (nonsoil [PM.sub.2.5]) was also related with cardiovascular mortality with 1 day lag (p [is less than] 0.10). Table 7 lists all of the statistically significant associations with cardiovascular mortality. Cardiovascular mortality showed a more consistent association with particulate mass concentrations than total mortality. We further investigated the associations between the mortality outcomes and PM by evaluating the association between the mortality outcomes and the PM composition. The [PM.sub.2.5] composition data analysis revealed that EC and TC were significantly associated with cardiovascular mortality (1 day lag). Weaker associations were also evident with OC at 1 and 3 days lag and TC at 3 days lag. [K.sub.S] had a significant positive association with cardiovascular mortality (3 day lag). We also found that soil, S, and Pb were negatively associated with total mortality. 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 factors are presented in Table 8. Factor 1 probably represents the influence of motor vehicle exhaust and resuspended road dust with high loadings (loading [is greater than] 0.5) on Mn, Fe, Zn, Pb, OC, EC, CO, and [NO.sub.2]. Factor 2 represents soil with high loadings on Al, Si, and Fe. Factor 3 represents vegetative burning with high loadings on OC and [K.sub.S]. Factor 4 represents a local source of [SO.sub.2] with a high loading on [SO.sub.2]. Factor 5 represents predominately regional sulfate sulfate, chemical compound containing the sulfate (SO4) radical. Sulfates are salts or esters of sulfuric acid, H2SO4, formed by replacing one or both of the hydrogens with a metal (e.g., sodium) or a radical (e.g., ammonium or ethyl). with a high loading on S. The RRs 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 [SO.sub.2] had a negative association with total mortality. We also found a significant negative association for fine soil on lag days 1 and 2, and a nearly significant negative association on lag days 3 and 4. Cardiovascular disease was significantly associated with the factors representing motor vehicles (lag day 1) and vegetative burning (lag day 3). Regression analysis with all of the factors included in a multisource model produced similar results.
Table 8. Loadings from factor analysis.
Factor Factor Factor Factor Factor
1 2 3 4 5
Al 0.14 0.96 0.08 -0.01 0.07
Si 0.19 0.96 0.11 -0.01 0.10
S 0.04 0.15 0.01 -0.03 0.96
Ca 0.26 0.93 0.15 -0.01 0.09
Mn 0.66 0.62 0.05 0.13 0.07
Fe 0.57 0.76 0.19 0.19 0.05
Zn 0.86 0.24 0.03 -0.03 0.03
Br 0.46 0.31 0.59 0.01 0.28
Pb 0.74 0.21 0.25 0.12 0.26
OC 0.66 0.23 0.55 0.33 0.01
EC 0.76 0.25 0.42 0.28 -0.08
[K.sub.S] 0.20 0.08 0.92 0.08 -0.04
CO 0.76 0.20 0.39 0.35 -0.09
[NO.sub.2] 0.69 0.24 0.31 0.45 -0.05
[SO.sub.2] 0.24 -0.04 0.09 0.93 -0.02
Percent 30.5 27.5 13.7 9.7 7.4
variance
explained
by factor
Table 9. RR for total and cardiovascular mortality from an interquartile range (IQR) increase in each factor.
Lag
Outcome, factor days [Beta] SE t
Total mortality
Factor 2 1 -0.03 0.01 -2.03
2 -0.04 0.01 -2.45
3 -0.02 0.01 -1.67
4 -0.02 0.01 -1.74
Factor 4 2 -0.03 0.01 -2.01
4 -0.03 0.01 -1.72
Factor 5 0 0.03 0.01 2.23
3 -0.03 0.01 -2.22
Cardiovascular
mortality
Factor 1 1 0.05 0.02 2.59
Factor 3 3 0.05 0.02 2.67
Factor 5 0 0.04 0.02 2.03
Outcome, factor IQR RR LCI UCI
Total mortality
Factor 2 1.26 0.96 0.93 1.00
1.26 0.96 0.92 0.99
1.26 0.97 0.94 1.01
1.26 0.97 0.94 1.00
Factor 4 1.09 0.97 0.94 1.00
1.09 0.97 0.94 1.00
Factor 5 1.38 1.04 1.01 1.08
1.39 0.96 0.92 0.99
Cardiovascular
mortality
Factor 1 1.11 1.06 1.01 1.10
Factor 3 1.02 1.05 1.01 1.09
Factor 5 1.38 1.06 1.00 1.12
Abbreviations: [Beta], regression coefficient; LCI, lower 95% confidence interval; t, t-statistic from the regression model; UCI, upper 95% confidence interval. Table 10 presents the results from the factor analysis on the daily concentrations of the chemical components of [PM.sub.CF] from samples collected by the dichotomous sampler. Factor 1 represents soil with high loadings on Al, Si, K, Ca, Mn, Fe, Sr, and Rb. Factor 2 represents a source of coarse fraction metals with high loadings on Zn, Pb, and Cu. Factor 13 represents a marine influence with a high loading on Cl. These three factors explain 91.8% of the variance in the [PM.sub.CF] data.
Table 10. Factor analysis results for [PM.sub.CF].
Factor Factor Factor
Element 1 2 3
Al 0.91 0.33 0.22
Si 0.90 0.36 0.24
Cl 0.25 -0.35 0.82
S 0.59 0.55 0.41
K 0.91 0.33 0.23
Ca 0.84 0.41 0.31
Mn 0.88 0.42 0.17
Fe 0.84 0.50 0.19
Zn 0.47 0.83 0.07
Br 0.23 0.30 0.85
Pb 0.40 0.80 -0.02
Sr 0.83 0.42 0.28
Cu 0.41 0.82 -0.02
Rb 0.91 0.27 0.17
Percent 51.1 26.5 14.2
variance
explained
by factor
Sensitivity analysis. As a sensitivity analysis, we analyzed temperature as a cofactor cofactor An atom, organic molecule, or molecular group that is necessary for the catalytic activity (see catalysis) of many enzymes. A cofactor may be tightly bound to the protein portion of an enzyme and thus be an integral part of its functional structure, or it may rather than a confounder con·found tr.v. con·found·ed, con·found·ing, con·founds 1. To cause to become confused or perplexed. See Synonyms at puzzle. 2. . That is, we evaluated the effects of temperature on mortality as an independent variable rather than adjusting for it in the model as a confounding variable A confounding variable (also confounding factor, lurking variable, a confound, or confounder) is an extraneous variable in a statistical or research model that should have been experimentally controlled, but was not. . We evaluated the significance of temperature after adjusting for day of the week, time trends, and relative humidity. For total and cardiovascular mortality, we found that temperature was not associated with excess deaths. Temperature was not correlated with either [PM.sub.10] (r = -0.08) or [PM.sub.2.5] (r = -0.25). A second analysis examined the effect of extreme temperatures. If the average daily temperature was greater than or equal to the 95th percentile (35.4 [degrees] C), we assigned a 1 to the predictor variable Noun 1. predictor variable - a variable that can be used to predict the value of another variable (as in statistical regression) variable quantity, variable - a quantity that can assume any of a set of values ; otherwise we assigned a 0. We did not find an association between extreme temperature and total mortality. However, with cardiovascular mortality, extreme temperature was associated with excess deaths at 0 and 2 days lag (p [is less than] 0.1). To further assess the importance of the high temperature days to our analysis, we evaluated the association between [PM.sub.2.5] and cardiovascular mortality after excluding the days when the temperature was above the 95th percentile. The effect of eliminating the high temperature days was negligible  This article or section is written like a personal reflection or and may require .Please [ improve this article] by rewriting this article or section in an . . The RR for cardiovascular mortality associated with [PM.sub.2.5] (1 day lag) including all days was the same as that excluding the hottest days (RR = 1.06; CI, 1.02-1.10). We also conducted a sensitivity analysis with relative humidity as a cofactor, with the model controlling for time trends and temperature. As a cofactor, relative humidity was not associated with either total mortality or cardiovascular mortality. To further assess the effects of extreme relative humidity, we eliminated the driest days (relative humidity [is less than] 25th percentile) from the data. We then found that the coarse fraction was no longer associated with total mortality. The association between cardiovascular mortality and coarse fraction was statistically significant (p [is less than] 0.05) on the concurrent day, but nonsignificant with 1-day lag. We also used dew point dew point: see dew. rather than relative humidity in the base model. Controlling for dew point rather than relative humidity did not alter our results. We obtained similar regression coefficients. To assess the effect of replacing the [PM.sub.2.5] cyclone on the TEOM with the WINS, we evaluated the association between soil and total mortality from 1 January 1995 to 31 December 1996 and from 1 January 1997 to 31 December 1997. The latter period represented the WINS inlet measurements. The association between soil and mortality was not significant for the cyclone measurements alone. Analysis with only the WINS data revealed that the association between soil and mortality was positive and significant at 0 days lag, but not significant for any of the other days. We estimated soil-related potassium using a correction ratio = K/Si (23). We then reevaluated the RR for cardiovascular mortality associated with [K.sub.S] using [K.sub.S] calculated from three slightly different values of K/Si. This correction ratio is dependent on where the soil was obtained: [PM.sub.2.5] paved road dust (K/Si = 1.85/13.69), an agricultural field (K/Si = 1.98/14.35), or Phoenix desert soil (K/Si = 1.89/14.00) (27). We found similar RRs for cardiovascular mortality associated with [K.sub.S] when we used any of these three approaches. In contrast, total potassium was not associated with either total or cardiovascular mortality. Discussion To our knowledge this is the first time-series analysis that has looked at the association between PM chemical composition and mortality and the association between the underlying factors influencing that composition and mortality. Ozkaynak and Thurston (28) combined source apportionment and epidemiologic methods to assess the effects of air pollution on mortality. However, their study was a cross-sectional analysis Cross-sectional analysis Assessment of relationships among a cross-section of firms, countries, or some other variable at one particular time. rather than a time--series analysis. The present study found significant associations between air pollutants and total nonaccidental and cardiovascular mortality. The association between [PM.sub.10] and cardiovascular mortality is consistent with previous studies. Zmirou et al. (17) reported an RR for cardiovascular mortality from a 50-[micro]g/[m.sup.3] increase in [PM.sub.10] (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 Noun 1. respiratory disease - a disease affecting the respiratory system respiratory disorder, respiratory illness adult respiratory distress syndrome, ARDS, wet lung, white lung - acute lung injury characterized by coughing and rales; inflammation of the death and cardiovascular deaths with [PM.sub.10] in Utah. Schwartz (2) also found that on 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 Birmingham (pronounced [ˈbɝmɪŋˌhæm]) is the largest city in the U.S. state of Alabama and is the county seat of Jefferson County. (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 [PM.sub.2.5] and cardiovascular mortality is similar to that of Schwartz et al. (3), who found that a 10-[micro]g/[m.sup.3] increase in [PM.sub.2.5] was associated with a 1.5% increase in total mortality and 2.1% increase in mortality from ischemic heart disease Ischemic heart disease Insufficient blood supply to the heart muscle (myocardium). Mentioned in: Myocarditis ischemic heart disease in a study of six eastern U.S. cities. In contrast to Schwartz et al. (3), the present study also found a significant association between [PM.sub.CF] and total and cardiovascular mortality. Although Schwartz et al. (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
Steubenville is a city located along the Ohio River in Jefferson County, Ohio, in the United States. , alone. Such observed differences may have been due to differences in regional coarse fraction composition. In Spokane, Schwartz et al. (12) also found no association between coarse particle concentration and total mortality. However, that study only looked at high episodes of coarse particle concentrations resulting from dust storms. Our findings are in agreement with Ostro et al. (13), who found a significant association between daily [PM.sub.10] dominated by coarse particles and mortality. We investigated the possibility that [PM.sub.CF] was a surrogate for dryness by eliminating the days with humidity humidity, moisture content of the atmosphere, a primary element of climate. Humidity measurements include absolute humidity, the mass of water vapor per unit volume of natural air; relative humidity (usually meant when the term humidity less than the 25th percentile. Although the association with total mortality was no longer significant, 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 [PM.sub.2.5] cyclone on the DFPSS was replaced with a WINS on 20 December 1996. The sharper cut point reduced the amount of soil intrusion into the [PM.sub.2.5] sample, which could produce soil data that 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 total mortality. After removing the WINS data, the association between reconstructed soil and total mortality was not significant. These observations are similar to that 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 soil was the least significant predictor of mortality. We also evaluated the association between soil and mortality with only soil data obtained with the WINS. The association was positive and significant (p [is less than] 0.05) on the concurrent day, but not significant on any other lag days. However, this observation may be due to the low number of days used to evaluate the association between WINS [PM.sub.2.5] soil and total mortality (n = 377). With respect to the elemental components of [PM.sub.2.5], we found that EC was significantly associated with cardiovascular mortality. EC is found in combustion-derived particles, most notably diesel exhaust (21). We found that Ks [potassium from vegetative burning (23)] was also associated with cardiovascular mortality. We found several associations that are potentially spurious spu·ri·ous adj. Similar in appearance or symptoms but unrelated in morphology or pathology; false. spurious simulated; not genuine; false. . The associations with these variables were found with only total mortality and not with cardiovascular mortality. Lead was negatively associated with total 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 accumulated in the soil or in road dust from the past use of leaded gasoline gasoline or petrol, light, volatile mixture of hydrocarbons for use in the internal-combustion engine and as an organic solvent, obtained primarily by fractional distillation and "cracking" of petroleum, but also obtained from natural gas, by . S was also negatively associated with total mortality on lag day 3. At present, the reason for the negative association with S is unclear. However, S accounts for a rela-tively small percentage of the mass of [PM.sub.2.5] (15%). The significant negative associations between total mortality and Pb and S were not consistent with the lack of association between these exposure variables and cardiovascular mortality. For the gaseous species, we found that total nonaccidental mortality and cardiovascular mortality were strongly associated with CO and [NO.sub.2]. These observations are similar to those of Burnett et al. (30), who found associations between CO and [NO.sub.2] and total nonaccidental mortality in Toronto, Canada. Burnett et al. (30) also found that cardiac mortality was associated with CO. CO exacerbates cardiac conditions (10). CO concentrations are also associated with hospital admissions for cardiovascular disease (31). In Phoenix the primary sources of CO and [NO.sub.2] are motor vehicles. The association between [SO.sub.2] and cardiovascular mortality was similar to that of Zmirou et al. (17), who also found that an increase in [SO.sub.2] was associated with an increase in cardiovascular deaths (RR = 1.04). In addition, Zmirou et al. (17) found weak but significant association between 1-hr maximum [O.sub.3] concentrations and cardiovascular mortality (RR = 1.02). Hoek et al. (32) also found an association between total mortality and O.sub.3] in the Netherlands. We found no significant associations with [O.sub.3]. The present study demonstrated the use of factor analysis in an epidemiologic study epidemiologic study A study that compares 2 groups of people who are alike except for one factor, such as exposure to a chemical or the presence of a health effect; the investigators try to determine if any factor is associated with the health effect . Using factor analysis, we were able to identify 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 particulate [SO.sub.4] concentrations were significantly associated with cardiovascular mortality. The soil factor, however, was associated with fewer than expected total deaths. These results are consistent with our time-series results for individual pollutants, specifically CO, [NO.sub.2], [K.sub.S], EC, OC, and reconstructed soil. It is interesting to note that the factor repre-senting S was significantly associated with cardiovascular mortality, whereas S alone in an individual pollutant model was not associated with cardiovascular mortality. This may be reflective of the contribution of Pb and Br to the S factor. A unique aspect of this study was the use of the chemical composition data of [PM.sub.2.5] Using such data, we found positive associations between cardiovascular mortality and [K.sub.S], OC, and EC as well as the more traditionally measured pollutants CO, [NO.sub.2], [SO.sub.2], [PM.sub.10], [PM.sub.2.5], and [PM.sub.CF]. Significant associations were also found with factors associated with incomplete combustion products and particulate S compounds. A limitation of this study is that the factor analysis results are only in terms of the variance explained by each factor, rather than in terms of the quantitative contribution from a specific source category. Although methods are available to include quantitative source apportionments in a time-series framework (33), such an analysis is beyond the scope of this initial investigation. REFERENCES AND NOTES (1.) Schwartz J. Air pollution and daily mortality in Birmingham, Alabama. Am J Epidemiol 137:1136-1146 (1993). (2.) Schwartz J. What are people dying of on high air pollution days? Environ Res 64:26-35 (1994). (3.) Schwartz J, Dockery D, Neas L. Is daily mortality associated specifically with fine particles Fine particles are an air pollutant mainly produced by cars running on diesel. Other sources are the combustion of fossil fuels in power plants and various industrial processes. ? J Air Waste Manag Assoc 46:927-939 (1996). (4.) Fairley D. The relationship of daily mortality to suspended particulates in Santa Clara Santa Clara, city, Cuba Santa Clara (sän`tä klä`rä), city (1994 est. pop. 217,000), capital of Villa Clara prov., central Cuba. County, 1980-1986. Environ Health Perspect 89:159-168 (1990). (5.) Pope CA, Schwartz J, Ransom ransom, price of redemption demanded by the captor of a person, vessel, or city. In ancient times cities frequently paid ransom to prevent their plundering by captors. The custom of ransoming was formerly sanctioned by law. MR. Daily mortality and [PM.sub.10] pollution in Utah Valley Utah Valley is a valley in North Central Utah located in Utah County, and is considered part of the Wasatch Front. It contains Provo, Orem, and their suburbs, including Spanish Fork and American Fork. Utah Lake is a natural shallow fresh water lake in its center. . Arch Environ Health 47:211-217 (1992). (6.) Dockery DW, Pope CA III CA III Challenge Athena version III (Navy SATCOM link) . Acute respirator respirator /res·pi·ra·tor/ (res´pi-ra?ter) ventilator (2). cuirass respirator see under ventilator. effects of particulate air pollution. Annu Rev Public Health 15:107-132 (1994). (7.) Ostro B. Fine particulate air pollution and mortality in two Southern California Southern California, also colloquially known as SoCal, is the southern portion of the U.S. state of California. Centered on the cities of Los Angeles and San Diego, Southern California is home to nearly 24 million people and is the nation's second most populated region, counties. Environ Res 70:98-104 (1995). (8.) Vedel S. Ambient Surrounding. For example, ambient temperature and humidity are atmospheric conditions that exist at the moment. See ambient lighting. particles and health: lines that divide. J Air Waste Manag Assoc 47:551-581 (1997). (9.) Wordley J, Walters S Wal·ters , Barbara Born 1931. American television newscaster and reporter. After working for the National Broadcasting Company (1963-1976), she joined the American Broadcasting Company (1976-1979) and became the first woman to anchor the nightly , Ayres JG. Short term variations in hospital admissions and mortality and particulate air pollution. Occup Environ Med 54:108-116 (1997). (10.) Pope CA III, Hill RW, Villegas GM. Particulate air pollution and daily mortality on Utah's Wasatch Front The Wasatch Front (Or Greater Wasatch) is an urban area in the U.S. state of Utah. It consists of a chain of cities and towns stretched along the Wasatch Range from approximately Santaquin in the south to Brigham City in the north. . Environ Health Perspect 107:567-573 (1999). (11.) Bascom R. Health effects of outdoor air pollution. Am J Respir Crit Care Med 153:477-498 (1996). (12.) Schwartz J, Norris G, Larson T, Sheppard L, Claiborne C, Koenig J. Episodes of high coarse particle concentrations are not associated with increased mortality. Environ Health Perspect 107:339-342 (1999). (13.) Ostro BD, Hurley S Hurley has become the English version of at least three distinct original Irish names: the Ó hUirthile, part of the Dál gCais tribal group, based in Clare and North Tipperary; the Ó Muirthile, based around Kilbritain in west Cork; and the OhIarlatha, from the district of , Lipsett MJ. Air pollution and daily mortality in the Coachella Valley, California: a study of [PM.sub.10] dominated by coarse particles. Environ Res 81:231-238 (1999). (14.) U.S. EPA. The Air Quality Criteria for Particulate Matter. EPA/600/P-95/001bF. Washington, DC:U.S. Environmental Protection Agency, 1996. (15.) Norris GA, Zweidinger RB, Bloemen HJTh, Purdue LJ, Bowser Bowser may mean:
(16.) U.S. Census Bureau Noun 1. Census Bureau - the bureau of the Commerce Department responsible for taking the census; provides demographic information and analyses about the population of the United States Bureau of the Census . U.S. Census 1990. Available: http://www.census.gov/cgi-bin/gazetteer [cited 18 January 2000]. (17.) Zmirou D, Schwartz J, Saez M, Zanobetti A, Wojtyniak B, Touloumi G, Spix C, Ponce de Leon Ponce de Le·ón , Juan 1460-1521. Spanish explorer who sailed with Columbus on his second voyage (1493-1494) and discovered Florida (1513) while looking for the legendary Fountain of Youth. Noun 1. A, Le Moullec Y, Bacharova L, et al. Time-series analysis of air pollution and cause-specific mortality. Epidemiology 9:495-503 (1998). (18.) Liao D, Creason J, Shy C, Williams R, Watts R, Zweidinger R. Daily variation of particulate air pollution and poor cardiac autonomic autonomic /au·to·nom·ic/ (aw?to-nom´ik) not subject to voluntary control. See under system. au·to·nom·ic adj. 1. Functionally independent; not under voluntary control. control in the elderly. Environ Health Perspect 107:521-525 (1999). (19.) Burnett RT, Cakmak S, Raizenne ME, Stieb D, Vincent R, Krewski D, Brook JR, Philips O, Ozkaynak H. The association between ambient carbon monoxide levels and daily mortality in Toronto, Canada. J Air Waste Manag Assoc 48:689-700 (1998). (20.) Touloumi G, Katsouyanni K, Zmirou D, Schwartz J, Spix C, de Leon AP, Tobias A, Quennel P, Rabczenko D, Bacharova L, et al. Short-term effects of ambient oxidant oxidant /ox·i·dant/ (ok´si-dant) the electron acceptor in an oxidation-reduction (redox) reaction. ox·i·dant n. See oxidizer. exposure on mortality: a combined analysis within the APHEA APHEA Australasian and Pacific Hansard Editors Association project. Am J Epidemiol 146:177-185 (1997). (21.) Birch birch, common name for some members of the Betulaceae, a family of deciduous trees or shrubs bearing male and female flowers on separate plants, widely distributed in the Northern Hemisphere. ME, Cary RA Elemental carbon-based method for monitoring occupational exposures to particulate diesel exhaust. Aerosol aerosol (âr`əsōl,–sŏl): see colloid. aerosol System of tiny liquid or solid particles evenly distributed in a finely divided state through a gas, usually air. Sci Technol 25:221-241 (1996). (22.) U.S. Environmental Protection Agency. Aerometric Information Retrieval System. Available: http:// www.epa.gov/airs/airs.html [cited 14 December 1999]. (23.) Lewis CW, Baumgardner RE, Stevens RK, Russworm GM. Receptor modeling study of Denver winter haze. Environ Sci Technol 20:1126-1136 (1986). (24.) Maim WC, Sisler JF, Huffman D, Eldred RA, Cahill TA. Spatial and seasonal trends in particle concentration and optical extinction extinction, in biology, disappearance of species of living organisms. Extinction occurs as a result of changed conditions to which the species is not suited. in the United States. J Geophys Res 99:1347-1370 (1994). (25.) Hastie T, Tibshirani R. Generalized Additive Models In statistics, the generalized additive model (or GAM) is a statistical model developed by Trevor Hastie and Rob Tibshirani blending properties of multiple regression (a special case of general linear model) with additive models. . London:Chapman and Hall Chapman and Hall was a British publishing house, founded in the first half of the 19th century by Edward Chapman and William Hall. Upon Hall's death in 1847, Chapman's cousin Frederic Chapman became partner in the company, of which he became sole manager upon the retirement of , 1990. (26.) Akaike H. Statistic statistic, n a value or number that describes a series of quantitative observations or measures; a value calculated from a sample. statistic a numerical value calculated from a number of observations in order to summarize them. predictor identification. Ann Inst Stat Math 22:203-217 (1970). (27.) Chow JC, Watson JG, Richards DW, Haase DL, McDade C, Dietrich DL, Moon D, Sloane C. The 1989-90 Phoenix [PM.sub.10] Study, Vol II. Source Apportionment Appendices ap·pen·di·ces n. A plural of appendix. , DRI See Digital Research. Document No. 8931 6F2. Reno, NV:Desert Research Institute, 1991. (28.) Ozkaynak H, Thurston G. Associations between 1980 U.S. mortality rates and alternative measures of airborne particle concentration. Risk Anal anal (a´n'l) relating to the anus. a·nal adj. 1. Of, relating to, or near the anus. 2. 7:449-461 (1987). (29.) Anderson HR, Ponce de Leon A, Bland JM, Bower JS, Strachan DP. Air pollution and daily mortality in London: 1987-92. Br Med J 312:665-669 (1996). (30.) Burnett RT, Smith-Doiron M, Stieb D, Cakmak S, Brook JR. Effects of particulate and gaseous air pollution on cardiorespiratory car·di·o·res·pi·ra·to·ry adj. Of or relating to the heart and the respiratory system. Adj. 1. cardiorespiratory - of or pertaining to or affecting both the heart and the lungs and their functions; "cardiopulmonary hospitalizations. Arch Environ Health 54:130-139 (1999). (31.) Schwartz J. Air Pollution and Hospital Admissions for Cardiovascular Disease in Tucson. Epidemiology 8:371-377 (1997). (32.) Hoek G, Schwartz JD, Groot B, Eilers P. Effects of ambient particulate matter and ozone on daily mortality in Rotterdam, the Netherlands. Arch Environ Health 52:455-463 (1997). (33.) Henry RC, Lewis CW, Collins JF. Vehicle-related hydrocarbon hydrocarbon (hī'drōkär`bən), any organic compound composed solely of the elements hydrogen and carbon. The hydrocarbons differ both in the total number of carbon and hydrogen atoms in their molecules and in the proportion of hydrogen source compositions from ambient data: the GRECE/SAFER Method. Environ Sci Technol 28:823-832 (1994). Address correspondence to T.F Mar, Department of Environmental Health, Box 357234, University of Washington, Seattle, WA 98195-7234 USA. Telephone: (206) 685-1596. Fax: (206) 685-3990. E-mail: therese@u.washington.edu We thank D. Bates Bates , Katherine Lee 1859-1929. American educator and writer best known for her poem "America the Beautiful," written in 1893 and revised in 1904 and 1911. for his advice and comments regarding the manuscript. We also thank T. Moore for advice regarding the representative spatial scale of the platform particulate matter measurements, C. Mrela for the mortality data, and B. O'Brian for help with assembling the data. This publication was made possible in part by grant 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 contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the NIEHS, NIH. This paper has been subjected to EPA review and approved for publication. Mention of trade names or commercial products does not constitute an endorsement or recommendation for use. Received 8 September 1999; accepted 9 November 1999. Therese F. Mar,(1) Gary A. Norris,(2) Jane Q. Koenig,(1) and Timothy V. Larson(3) (1) Department of Environmental Health, University of Washington, Seattle, Washington  This page is protected from moves until disputes have been resolved on the .The reason for its protection is listed on the protection policy page. , USA (2) U.S. Environmental Protection Agency, Research Triangle Park, North Carolina North Carolina, state in the SE United States. It is bordered by the Atlantic Ocean (E), South Carolina and Georgia (S), Tennessee (W), and Virginia (N). Facts and Figures Area, 52,586 sq mi (136,198 sq km). Pop. , USA (3) Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA |
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