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Factors affecting the association between ambient concentrations and personal exposures to particles and gases.



Results from air pollution exposure assessment studies suggest that ambient Surrounding. For example, ambient temperature and humidity are atmospheric conditions that exist at the moment. See ambient lighting.  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.  [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.
 with aerodynamic diameter 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.  [less than or equal to] 2.5 [micro]g ([PM.sub.2.5])], but not ambient gases, are strong proxies of corresponding personal exposures. For particles, the strength of the personal-ambient association can differ by particle component and level of home ventilation. For gases, however, such as ozone ([O.sub.3]), 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.
 (N[O.sub.2]), 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.  (S[O.sub.2]), the impact of home ventilation on personal-ambient associations is untested. We measured 24-hr personal exposures and corresponding ambient concentrations to [PM.sub.2.5], 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).  (S[O.sup.2-.sub.4]), elemental elemental

emanating from or pertaining to elements.


elemental diet
see elemental diet.
 carbon, [O.sub.3], N[O.sub.2], and S[O.sub.2] for 10 nonsmoking non·smok·ing  
adj.
1. Not engaging in the smoking of tobacco: nonsmoking passengers.

2. Designated or reserved for nonsmokers: the nonsmoking section of a restaurant.
 older adults in Steubenville, Ohio
For other locations with similar names, please see: Steuben.


Steubenville is a city located along the Ohio River in Jefferson County, Ohio, in the United States.
. We found strong associations between ambient particle concentrations and corresponding personal exposures. In contrast, although significant, most associations between ambient gases and their corresponding exposures had low slopes and [R.sup.2] values; the personal-ambient N[O.sub.2] association in the fall season was moderate. For both particles and gases, personal-ambient associations were highest for individuals spending most of their time in high- compared with low-ventilated environments. Cross-pollutant models indicated that ambient particle concentrations were much better surrogates for exposure to particles than to gases. With the exception of ambient N[O.sub.2] in the fall, which showed moderate associations with personal exposures, ambient gases were poor proxies for both gas and particle exposures. In combination, our results suggest that a) ventilation may be an important modifier (programming) modifier - An operation that alters the state of an object. Modifiers often have names that begin with "set" and corresponding selector functions whose names begin with "get".  of the magnitude of effect in time-series health studies, and b) results from time-series health studies based on 24-hr ambient concentrations are more readily interpretable for particles than for gases. Key words: air pollution, ambient concentration, confounding confounding

when the effects of two, or more, processes on results cannot be separated, the results are said to be confounded, a cause of bias in disease studies.


confounding factor
, 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 , nitrogen dioxide, ozone, particle components, personal exposure, [PM.sub.2.5], sulfur dioxide. doi:10.1289/ehp.8422 available via http://dx.doi.org/[Online 15 December 2005]

**********

Air pollution exposure assessment studies consistently show associations between ambient fine particle [particulate matter with aerodynamic diameter [less than or equal to] 2.5 [micro]g ([PM.sub.2.5])], concentrations and corresponding personal exposures for panels of individuals, particularly for regional [PM.sub.2.5] components such as sulfate (S[O.sup.2-.sub.4]) and for those living in well-ventilated homes (Janssen et al. 2000; Rojas-Bracho et al. 2000; Sarnat et al. 2000). Results from these studies suggest that ambient [PM.sub.2.5] concentrations are strong proxies of corresponding exposures but that this ability differs by particle component and home ventilation status. In contrast, studies examining gases, such as ozone ([O.sub.3]), nitrogen dioxide (N[O.sub.2]), and sulfur dioxide (S[O.sub.2]), consistently show ambient gas concentrations to be poor proxies of corresponding exposures (Brauer et al. 1989; Linaker et al. 2000; Liu et al. 1997; Patterson and Eatough 2000; Sarnat et al. 2001). The impact of home ventilation status on the relationship between ambient and personal gas concentrations, however, is untested, leaving open the possibility that ambient gas concentrations may better reflect corresponding personal exposures under certain conditions or for some segments of the population.

In this study, we used data collected in our study of older adults living in Steubenville, Ohio, to examine the impact of season, home ventilation, and particle composition on associations between ambient concentrations and corresponding personal exposures to both [PM.sub.2.5] and gases. In cross-pollutant models, we examined associations between ambient [PM.sub.2.5] concentrations and personal gas exposures and vice versa VICE VERSA. On the contrary; on opposite sides. . We discuss the implications of our findings for the results of time-series health studies.

Materials and Methods

Study design and subject characteristics. Exposure monitoring was performed in Steubenville, Ohio, for 23 weeks during the summer (4 June-18 August) and fall (24 September-15 December) of 2000 under a protocol approved by the Harvard School of Public Health The Harvard School of Public Health is (colloquially, HSPH) is one of the professional graduate schools of Harvard University. Located in Longwood Area of the Boston, Massachusetts neighborhood of Mission Hill, next to Harvard Medical School and Cambridge, Massachusetts, . Ten nonsmoking, senior adults gave written informed consent before their participation in our study each season; five subjects participated in both seasons. With the exception of two individuals who lived in single-family homes, all subjects lived in one of three centrally-located apartment buildings. The 15 subjects formed a subset A group of commands or functions that do not include all the capabilities of the original specification. Software or hardware components designed for the subset will also work with the original.  of our larger cohort cohort /co·hort/ (ko´hort)
1. in epidemiology, a group of individuals sharing a common characteristic and observed over time in the group.

2.
 (n = 32; mean age, 71.8 years) participating in a more extensive exposure and cardiovascular health study. To allow their participation in health monitoring, we conducted cardiovascular health screening on all subjects before their inclusion. We treated all subjects in an ethical manner.

For each subject, we collected two consecutive 24-hr (0900-0900 hr) personal exposure measurements during each week of the study. The first 24-hr measurement for each subject began on Monday through Thursday, with each subject sampled on the same 2 days of each week. Our target sample number was 220 in the summer and 240 in the fall. On days when we collected personal exposure measurements, we also conducted concurrent 24-hr (0900-0900 hr) ambient monitoring at a central monitoring site located within 1 mile of all subjects' residences.

Sampling methods. We measured personal and ambient [PM.sub.2.5], S[O.sup.2-.sub.4], elemental carbon (EC), [O.sub.3], N[O.sub.2], and S[O.sub.2] concentrations simultaneously using the Harvard multi-pollutant (MP) 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.  (Demokritou et al. 2001). The sampler consisted of two (duplicate DUPLICATE. The double of anything.
     2. It is usually applied to agreements, letters, receipts, and the like, when two originals are made of either of them. Each copy has the same effect.
) impaction-based personal environmental monitors (PEMs) for [PM.sub.2.5] and two impaction-based mini-PEMs for S[O.sup.2-.sub.4] and EC. A single sampling pump pulled air through the sampler. Greased grease  
n.
1. Soft or melted animal fat, especially after rendering.

2. A thick oil or viscous substance, especially when used as a lubricant.

3.
a. The oily substance present in raw wool; suint.
 impactor plates were used to minimize particle bounce. PEMs contained 37-mm Teflon filters (Gelman Sciences, Ann Arbor Ann Arbor, city (1990 pop. 109,592), seat of Washtenaw co., S Mich., on the Huron River; inc. 1851. It is a research and educational center, with a large number of government and industrial research and development firms, many in high-technology fields such as , MI) for the collection of [PM.sub.2.5]. Mini-PEMs contained 15-mm fluoropore filters for the collection of S[O.sup.2-.sub.4] and quartz fiber filters for the collection of EC. For ambient sampling, we split flows from the sampling pump (Medo USA Inc., Hanover Park, IL) into four air streams: 0.8 L/min to each of the mini-PEMs and 4.0 L/min to each of the PEMs. We similarly split flows for personal sampling into four air streams, with a lower flow to each PEM (Privacy Enhanced Mail) A standard for secure e-mail on the Internet. It supports encryption, digital signatures and digital certificates as well as both private and public key methods. Not widely used, work on PEM later evolved into S/MIME. See MIME.  (1.8 L/min) to allow the use of a single personal pump (BGI BGI Barclays Global Investors
BGI Bainbridge Graduate Institute
BGI Bureau Gravimétrique International
BGI Borland Graphic Interface (File Name Extension)
BGI Bridgetown, Barbados - Grantley Adams International
 400; BGI Inc., Waltham, MA). The MP sampler also consisted of passive [O.sub.3] and N[O.sub.2]/S[O.sub.2] badges. Each passive sampler contained a cellulose cellulose, chief constituent of the cell walls of plants. Chemically, it is a carbohydrate that is a high molecular weight polysaccharide. Raw cotton is composed of 91% pure cellulose; other important natural sources are flax, hemp, jute, straw, and wood.  filter coated with either nitrite nitrite

Any salt or ester of nitrous acid (HNO2). The salts are inorganic compounds with ionic bonds, containing the nitrite ion (NO2) and any cation.
 for the collection of [O.sub.3] (Koutrakis et al. 1993) or tri-ethanolamine for the collection of N[O.sub.2] and S[O.sub.2] (Ogawa 1998).

We affixed af·fix  
tr.v. af·fixed, af·fix·ing, af·fix·es
1. To secure to something; attach: affix a label to a package.

2.
 the MP sampler to a tripod for ambient monitoring, approximately 1 m above ground level. Ambient flow rates were measured before and after sampling with a precalibrated rotameter (Matheson 406; Matheson Tri-Gas, Montgomeryville, PA). To collect personal exposure samples, we affixed the MP sampler to the shoulder strap of a small bag used to carry the sampling pump, battery, and motion sensor. Personal flow rates were measured in duplicate pre- and postsampling using a mini-BUCK calibrator calibrator

an instrument for dilating a tubular structure or for determining the caliber of such a structure.
 (A.P. Buck Inc., Orlando, FL). We asked subjects to wear the sampler over their shoulder for as much time as possible and to complete a time-activity diary for each 24 hr sampling session.

We determined [PM.sub.2.5] concentrations gravimetrically at the Harvard School of Public Health, with Teflon filters weighed in duplicate before and after sample collection on an electronic 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
 (model C-31; Cahn Instruments, Cerritos, CA). Before each weighing, we equilibrated the filters in a room with controlled temperature (70 [+ or -] 5[degrees]F) 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.
 (40 [+ or -] 5%). Fluoropore and cellulose filters were 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.
 by ion chromatography Ion-exchange chromatography (or ion chromatography) is a process that allows the separation of ions and polar molecules based on the charge properties of the molecules.  (DX-100 and DX-120; Dionex Corp., Sunnyvale, CA), and quartz filters were analyzed for EC by thermal optical transmission (Sunset Laboratory Thermal Optical Transmittance Analyzer analyzer /ana·ly·zer/ (an´ah-li?zer)
1. a Nicol prism attached to a polarizing apparatus which extinguishes the ray of light polarized by the polarizer.

2.
; Sunset Laboratory, Inc., Tigard, OR) by CONSOL Energy CONSOL Energy is a coal and mining company based suburban Pittsburgh, USA. The head office is located in the southern suburb of Upper St.Clair, Pennsylvania CONSOL Energy Inc. is the largest producer of high-Btu bituminous coal in the United States.  Inc. (Pittsburgh, PA). CONSOL reported concentrations that fell below the analytical detection limit as "not detected."

Data processing data processing or information processing, operations (e.g., handling, merging, sorting, and computing) performed upon data in accordance with strictly defined procedures, such as recording and summarizing the financial transactions of a  and quality assurance. We invalidated in·val·i·date  
tr.v. in·val·i·dat·ed, in·val·i·dat·ing, in·val·i·dates
To make invalid; nullify.



in·val
 duplicate measurements for which the [PM.sub.2.5] concentrations differed by > 50% because large relative differences likely reflected sampling problems. We also invalidated corresponding S[O.sup.2-.sub.4] and EC concentrations, as the same pump provided airflow through these samplers. Five EC and five N[O.sub.2] samples were excluded from the data set based on deviations from their respective time-series and as statistical outliers (> 95% from the mean). The data validity for all pollutants pollutants

see environmental pollution.
 ranged between 90 and 99%.

Table 1 presents limits of detection (LOD Lod (lōd), city (1994 pop. 51,200), central Israel. It is also known as Lydda. Its manufactures include paper products, chemicals, oil products, electronic equipment, processed food, and cigarettes. ), precision, and accuracy of the collected data. We blank-corrected all samples by season and by microenvironment microenvironment /mi·cro·en·vi·ron·ment/ (-en-vi´ron-ment) the environment at the microscopic or cellular level.  as appropriate. We estimated field LODs for [PM.sub.2.5] as 3 times the standard deviation In statistics, the average amount a number varies from the average number in a series of numbers.

(statistics) standard deviation - (SD) A measure of the range of values in a set of numbers.
 of field blanks divided by the target flow rates and 24-hr sampling duration. Imprecision im·pre·cise  
adj.
Not precise.



impre·cisely adv.
 of the [PM.sub.2.5] measurements, determined using regression analyses of duplicate [PM.sub.2.5] measurements [i.e., (1 - slope) x 100%], was low, with values of 0-2%. Final [PM.sub.2.5] concentrations were calculated as the average of the valid duplicate [PM.sub.2.5] measurements.

For the remaining pollutants, many blanks had values below their respective analytical LODs. As a result, we calculated field LODs using the 96th 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
 of field blanks divided by the target flow rates and 24-hr sampling duration. For the passive samplers, we used predetermined pre·de·ter·mine  
v. pre·de·ter·mined, pre·de·ter·min·ing, pre·de·ter·mines

v.tr.
1. To determine, decide, or establish in advance:
 collection rates: 11 cc/min for [O.sub.3] (Chang et al. 1999), 13.3 cc/min for N[O.sub.2] (Chang et al. 1999), and 9.9 cc/min for S[O.sub.2] (Chang LT, personal communication, 2001). We estimated the imprecision for S[O.sup.2-.sub.4], EC, [O.sub.3], N[O.sub.2], and S[O.sub.2] samples as discussed by Kinney and Thurston (1993) using collocated ambient measurements for samples with values greater than the field LOD. Imprecision estimates for these measurements were larger (10-25%) than those for [PM.sub.2.5] ([less than or equal to] 2%), likely because of the lack of true duplicate sampling for these pollutants and also the inherently greater imprecision of passive sampling methods for the gases.

We determined the accuracy of the [PM.sub.2.5], [O.sub.3], N[O.sub.2], and S[O.sub.2] measurements as the ratio of mean MP and collocated reference method measurements multiplied mul·ti·ply 1  
v. mul·ti·plied, mul·ti·ply·ing, mul·ti·plies

v.tr.
1. To increase the amount, number, or degree of.

2. Mathematics To perform multiplication on.
 by 100%, using samples with concentrations greater than the field LOD. Reference measurements were not available for determining the accuracy of S[O.sup.2-.sub.4] and EC measurements.

Data analysis. We used MS Excel 2000 (Microsoft Corp., Redmond, WA), 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.  Release 8.02 (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. , Cary, NC), and S-PLUS 2000 Professional Release 3 (Insightful Corp., Seattle, WA) for all data analyses. Because values below the analytical LOD were not provided by the laboratory, we assigned values to nondetect samples up to each pollutant's analytical LOD as follows: a) for nondetect [O.sub.3], N[O.sub.2], and S[O.sub.2] samples, we assigned values by sampling from a distribution of values obtained during our previous MP exposure 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.
 (Sarnat et al. 2000); b) because no EC data from previous studies existed, we assigned values to nondetect samples using Excel's random number generator A program routine that produces a random number. Random numbers are created easily in a computer, since there are many random events that take place such as the duration between keystrokes. .

Given previous findings showing season to be an important modifier of air pollution concentrations in Steubenville (Connell et al. 2005), as well as home ventilation (Murray and Burmaster 1995), we stratified stratified /strat·i·fied/ (strat´i-fid) formed or arranged in layers.

strat·i·fied
adj.
Arranged in the form of layers or strata.
 all analyses by season. We summarized ambient pollutant pol·lut·ant
n.
Something that pollutes, especially a waste material that contaminates air, soil, or water.
 concentrations and examined associations between ambient particles and gases using models that accounted for correlation over time (PROC (language) PROC - The job control language used in the Pick operating system.

["Exploring the Pick Operating System", J.E. Sisk et al, Hayden 1986].
 MIXED in SAS using an exponential 1. (mathematics) exponential - A function which raises some given constant (the "base") to the power of its argument. I.e.

f x = b^x

If no base is specified, e, the base of natural logarthims, is assumed.
2.
 covariance Covariance

A measure of the degree to which returns on two risky assets move in tandem. A positive covariance means that asset returns move together. A negative covariance means returns vary inversely.
 structure, whereby the covariance among two observations taken at times [t.sub.j] and [t.sub.k] is [MATHEMATICAL EXPRESSION A group of characters or symbols representing a quantity or an operation. See arithmetic expression.  NOT REPRODUCIBLE re·pro·duce  
v. re·pro·duced, re·pro·duc·ing, re·pro·duc·es

v.tr.
1. To produce a counterpart, image, or copy of.

2. Biology To generate (offspring) by sexual or asexual means.
 IN ASCII ASCII or American Standard Code for Information Interchange, a set of codes used to represent letters, numbers, a few symbols, and control characters. Originally designed for teletype operations, it has found wide application in computers. ]).

We summarized subjects' time-activity and personal exposure data and calculated personal: ambient concentration ratios for comparing pollutant levels. We examined associations between ambient concentrations and personal exposures using linear mixed-effect models (PROC MIXED in SAS), with ambient concentrations modeled as fixed effects and subjects modeled as random effects Random effects can refer to:
  • Random effects estimator
  • Random effect model
. We examined the effect of home ventilation on the personal-ambient associations using "open window status" as a categorical That which is unqualified or unconditional.

A categorical imperative is a rule, command, or moral obligation that is absolutely and universally binding.

Categorical is also used to describe programs limited to or designed for certain classes of people.
 variable based on whether subjects spent "no time" or "any time" in indoor environments with open windows during the 24-hr sampling periods. We did not consider ventilation a continuous variable because of the large fraction of samples (21% in summer and 48% in fall) that contained subjects who spent all of their time indoors with closed windows. We included open window status in our personal--ambient models as a main effect and as an interaction term with ambient concentrations. Our models also included a "building" effect to control for differences in the characteristics of the buildings in which subjects resided. To minimize the influence of known indoor sources, we restricted models predicting personal N[O.sub.2] exposures to subjects without gas stoves in their homes. In addition, because of the large number of nondetect ambient S[O.sub.2] samples, we restricted models using ambient S[O.sub.2] as the independent variable to data above the analytical LOD.

For dependent variables in 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. , use of assigned values for nondetect samples may cause bias in parameter (1) Any value passed to a program by the user or by another program in order to customize the program for a particular purpose. A parameter may be anything; for example, a file name, a coordinate, a range of values, a money amount or a code of some kind.  estimates and their variances unless the proportion of assigned values is low (e.g., [less than or equal to] 10%) (Lubin et al. 2004). To avoid potential bias in models predicting personal exposures with extreme numbers of nondetect values (i.e., [O.sub.3] exposures in the fall and S[O.sub.2] exposures in both seasons, for which > 30% of values were nondetect; Table 2), we additionally used Tobit mixed-effect regression (survReg in S-Plus), a procedure for truncated truncated adjective Shortened  data (Tobin 1958). In these models, the obmd value y is censored cen·sor  
n.
1. A person authorized to examine books, films, or other material and to remove or suppress what is considered morally, politically, or otherwise objectionable.

2.
 below the analytical LOD:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII] [1]

The Tobit model The Tobit Model is an econometric, biometric model proposed by James Tobin (1958) to describe the relationship between a non-negative dependent variable  is subsequently based on the latent variable model A latent variable model is a statistical model that relates a set of variables (so-called manifest variables) to set of latent variables.

It is assumed that 1) the responses on the indicatiors or manifest variables are the result of an individual's position on the
:

[y.sup.*] = [beta]'x + b + u, [2]

where b ~ N(0,[[sigma].sup.2.sub.b]) and u ~ N(0,[[sigma].sup.2]), which estimates the effect of x on [y.sup.*] and describes the association as if all data were observable ob·serv·a·ble  
adj.
1. Possible to observe: observable phenomena; an observable change in demeanor. See Synonyms at noticeable.

2.
.

We report slopes, SEs, and t-values from all mixed models. We additionally report coefficient of determination Coefficient of determination

A measure of the goodness of fit of the relationship between the dependent and independent variables in a regression analysis; for instance, the percentage of variation in the return of an asset explained by the market portfolio return. Also known as R-square.
 ([R.sup.2]) values using a method developed by Xu (2003) for random intercept intercept

in mathematical terms the points at which a curve cuts the two axes of a graph.
 mixed models:

[R.sup.2] = 1 - [[sigma].sup.2]/[[beta].sup.2][[sigma].sup.2.sub.x] + [[sigma].sup.2], [3]

where [beta] is the slope of the model, [[sigma].sup.2] is the residual variance Residual variance or unexplained variance is part of the variance of any residual. The other part is explained variance. In analysis of variance and regression analysis, residual variance is that part of the variance which cannot be attributed to specific causes. , and [[sigma].sup.2.sub.x] is the variance of the independent variable.

Results

Ambient pollutant concentrations by season. Ambient [PM.sub.2.5] concentrations were comparable during both seasons, with averages of 20.1 ([+ or -] 9.3) [micro]g/[m.sup.3] during the summer and 19.3 ([+ or -] 12.2) [micro]g/[m.sup.3] during the fall (Table 2). Ambient S[O.sup.2-.sub.4] (expressed as ammonium sulfate ammonium sulfate, chemical compound, (NH4)2SO4, a colorless-to-gray, rhombohedral crystalline substance that occurs in nature as the mineral mascagnite. It is soluble in water and insoluble in alcohol or liquid ammonia. ) comprised a large fraction of the total [PM.sub.2.5] mass, with contributions of 52 and 43% in the summer and fall, respectively. Ambient EC, in contrast, comprised only 6% of the [PM.sub.2.5] mass in either season. The composition of [PM.sub.2.5] reflects the pollutant sources in the Steubenville region, which include numerous coal-fired power plants that contribute to S[O.sup.2-.sub.4] but little motor vehicle traffic that contributes to EC concentrations.

Among the gases, ambient [O.sub.3] concentrations showed the greatest seasonal differences, with considerably higher mean concentrations during the summer (29.3 [+ or -] 13.4 ppb ppb
abbr.
parts per billion
) compared with the fall (16.0 [+ or -] 8.1 ppb). The higher summertime concentrations likely reflect the importance of photochemical photochemical

in laser treatment, the laser light is absorbed and converted into chemical energy.
 processes for [O.sub.3] production.

Correspondingly, we found significant summertime associations among ambient [PM.sub.2.5], S[O.sup.2-.sub.4], and [O.sub.3] concentrations, which likely were due to the common photochemical formation processes of these secondary pollutants (Table 3). During the fall, associations between ambient particles and [O.sub.3] were negative, which may be due to the 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
 conditions during this season. Steubenville experiences considerable inversions during the fall, which can trap PM and local pollutants to the ground while preventing mixing with the air aloft containing regional pollutants such as [O.sub.3] (Connell et al. 2005). Associations between ambient particles and N[O.sub.2] and S[O.sub.2] were significant only during the fall; the association between ambient EC and N[O.sub.2], both traffic-related pollutants, was particularly strong and positive (t-value = 11.39).

Personal pollutant exposures and time-activity characteristics. We collected 8-24 repeated measurements per subject in each season, for a total of 194 measurements in the summer and 228 measurements in the fall. The total sample number per pollutant differed slightly because of pollutant-specific data invalidations (Table 2). On average, personal [PM.sub.2.5], EC, and N[O.sub.2] exposures were slightly higher than corresponding ambient levels. Mean personal:ambient ratios for [PM.sub.2.5] (ratio = 1.14), EC (ratio = 1.15), and N[O.sub.2] (ratio = 2.05 and 1.27, for subjects with and without gas stoves in their homes, respectively) all exceeded 1, likely because of influences of indoor sources. Spatial variability Spatial variability is characterized by different values for an observed attribute or property that are measured at different geographic locations in an area. The geographic locations are recorded using GPS (global positioning systems) while the attribute's spatial variability is  in ambient concentrations may add itionally explain these results. Although the central site was within 1 mile of subjects' residences, the site was higher in elevation elevation, vertical distance from a datum plane, usually mean sea level to a point above the earth. Often used synonymously with altitude, elevation is the height on the earth's surface and altitude, the height in space above the surface.  and may have experienced slightly lower ambient concentrations. For pollutants without significant indoor sources, S[O.sup.2-.sub.4] and, in particular, [O.sub.3] mean personal:ambient ratios were lower than 1 (0.75 and 0.24, respectively).

During the exposure sampling periods, subjects spent most of their time indoors (summer = 90.5%, fall = 95.2%) and at home (> 77%) during both seasons. When subjects were indoors, windows were open on average for 37.6% ([+ or -] 32.0%) of the time in the summer and 22.6% ([+ or -] 33.4%) in the fall. Although these results suggest that subjects spent more time in well-ventilated environments during the summer compared with the fall, it should be noted that subjects also spent more time in air-conditioned environments during the summer (39.8 [+ or -] 33.3%) compared with the fall (10.9 [+ or -] 19.6%). Time spent outdoors, in transit, and near particle sources (i.e., cooking, cleaning, near a smoker smoker A person who smokes tobacco, almost always understood to be cigarettes Ratio of ♂:♀ smokers Philippines64/19, China61/7, Saudi Arabia53/2, Russia50/12 ) was minimal ([less than or equal to] 7.0%) during both seasons.

Associations between personal exposures and ambient concentrations. [PM.sub.2.5], S[O.sup.2-.sub.4], and EC. Table 4 presents the slopes from regressions of ambient concentrations on corresponding personal exposures for the particle measures [PM.sub.2.5], S[O.sup.2-.sub.4], and EC. Associations between ambient [PM.sub.2.5] concentrations and corresponding personal exposures were strong, with high slopes and [R.sup.2] and t-statistics (t-value > 13.32). The association varied slightly by season, with a slope of 0.73 ([+ or -] 0.05) in the summer and 0.63 ([+ or -] 0.05) in the fall. Personal-ambient S[O.sup.2-.sub.4] slopes (summer = 0.74 [+ or -] 0.02; fall = 0.64 [+ or -] 0.02) were similar to those for [PM.sub.2.5] during both seasons, with stronger associations than those found for [PM.sub.2.5] (t-value > 26.36). The strong S[O.sup.2-.sub.4] associations are consistent with previous findings (Ebelt et al. 2000; Sarnat et al. 2000) and are likely because S[O.sup.2-.sub.4] is a stable particle with few indoor sources.

The slope of the personal-ambient EC association for the fall (0.70 [+ or -] 0.06) was also similar to that for total [PM.sub.2.5], but it was substantially lower in the summer (0.33 [+ or -] 0.10).

The lower summertime slope suggests a lower effective penetration efficiency for EC compared with other particle measures in the summer. Reasons for this lower association are unclear. It should be noted, however, that greater noise in the personal and ambient EC measurements during the summer likely decreased the strength of the summertime EC association because of the well-known downward bias of slopes in the presence of measurement error. Summertime EC measurements showed a very high field LOD, at approximately 50% of mean EC exposures (Table 1), which likely contributed to the lower t-statistic of the personal-ambient EC association during the summer (t-value = 3.24) compared with the fall (t-value = 12.43).

[O.sub.3], N[O.sub.2], and S[O.sub.2]. Slopes of personal--ambient regressions were low but statistically significant for each of the measured gases for both seasons, with the exception of summertime S[O.sub.2] (Table 4). Slopes in the fall were approximately twice those in the summer. The personal-ambient N[O.sub.2] slope, for example, was 0.25 ([+ or -] 0.06) in the summer and 0.49 ([+ or -] 0.05) in the fall. For all gases in both seasons, however, slopes and [R.sup.2] values were generally much lower than those found for particles.

Influence of ventilation conditions on personal--ambient associations. Home ventilation was an important modifying factor for many of the personal-ambient relationships, with highest slopes and strongest associations observed for subjects spending time "Spending Time" is the first single released by Christian artist Stellar Kart.

The lyrics describe the band members desire to spend "more time with God". "Sometimes it’s a real struggle to spend time with God.
 indoors with open windows (Table 5). The influence of home ventilation was particularly evident in the summer for S[O.sup.2-.sub.4] and for [O.sub.3]. The slope of the regression between ambient [O.sub.3] concentrations and corresponding personal [O.sub.3] exposures for individuals spending time in indoor environments with open windows (slope = 0.18 [+ or -] 0.03, t-value = 7.34), for example, was twice that of individuals spending no time indoors with open windows (slope = 0.08 [+ or -] 0.04, t-value = 1.89). The stronger associations and higher slopes during conditions in which homes were well ventilated ven·ti·late  
tr.v. ven·ti·lat·ed, ven·ti·lat·ing, ven·ti·lates
1. To admit fresh air into (a mine, for example) to replace stale or noxious air.

2.
 was probably because [O.sub.3], a reactive reactive /re·ac·tive/ (re-ak´tiv) characterized by reaction; readily responsive to a stimulus.

re·ac·tive
adj.
1. Tending to be responsive or to react to a stimulus.

2.
 pollutant, could penetrate indoors more efficiently during these conditions. Even in well-ventilated conditions, however, the slope of the [O.sub.3] association (0.18) was small, suggesting only minor changes in exposure associated with a reasonable change in outdoor concentrations. This may also reflect the reactivity of [O.sub.3] because, under the same conditions, the slope for S[O.sup.2-.sub.4] was 0.77.

Associations between ambient PM concentrations and personal gas exposures. Table 6 shows results from cross-pollutant analyses examining associations between ambient particles and personal gas exposures. Associations between ambient [PM.sub.2.5] concentrations and personal gas exposures were significant for [O.sub.3] in both seasons and for N[O.sub.2] in the fall. Although significant, however, the slopes for the associations were quite low (slopes < 0.17) and indicate that 24-hr personal [O.sub.3] and N[O.sub.2] exposures increased on average by only 1.1 and 1.7 ppb with every 10 [micro]g/[m.sup.3] increase in ambient [PM.sub.2.5]. Associations were also significant between the specific ambient particle components and personal [O.sub.3] and N[O.sub.2] exposures. Ambient particles were not significant predictors of personal S[O.sub.2] levels.

Associations between ambient gas concentrations and personal PM exposures. Table 7 shows results from cross-pollutant analyses examining associations between ambient gas concentrations and personal particle exposures. Several associations between ambient [O.sub.3] and S[O.sub.2] concentrations and personal particle exposures were significant, although the slopes and [R.sup.2] values were low ([R.sup.2] < 0.16). Associations between ambient N[O.sub.2] concentrations and personal particle exposures were significant in the fall, in particular for EC (t-value = 13.6, [R.sup.2] = 0.49). The slopes for the associations with ambient N[O.sub.2] were moderate, suggesting that 24-hr personal exposures to [PM.sub.2.5] increased by 9.3 [micro]g/[m.sup.3] for each 10-ppb increase in ambient N[O.sub.2].

Linear versus Tobit regression model results. Results of models predicting personal [O.sub.3] in the fall and personal S[O.sub.2] in both seasons were similar when running linear (Tables 4, 6) compared with Tobit (Table 8) mixed-effect regressions. The results suggest that bias was minimal for the linear regressions Linear regression

A statistical technique for fitting a straight line to a set of data points.
, which used data with assigned values for nondetect samples. Even though 32-54% of values were nondetect for personal [O.sub.3] and S[O.sub.2] exposures, randomly sampling from a known distribution appears to have been an adequate method for assigning values to these data series (Lubin et al. 2004).

Discussion

In Steubenville, we found 24-hr ambient particle concentrations to be consistently strong proxies of corresponding personal exposures, regardless of the particle species, season, and ventilation status. Associations between ambient concentrations and corresponding personal exposures were strongest for S[O.sup.2-.sub.4], a regional pollutant with no major indoor sources. Ambient concentrations of EC were also significant proxies of corresponding exposures, although associations were weaker, likely due to the influence of local sources such as traffic and cooking. Personal--ambient associations for particles were highest for subjects spending time indoors where windows were open compared with those spending time indoors where windows were closed. Our findings are consistent with those of previous studies (Ebelt et al. 2000; Janssen et al. 2000; Rojas-Bracho et al. 2000, 2004; Sarnat et al. 2000) and provide additional justification for the use of ambient [PM.sub.2.5], S[O.sup.2-.sub.4], and to a lesser extent EC, to represent corresponding mean personal exposures in 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
 analyses. Measurement error in epidemiologic studies 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  is known to bias the effect size estimates, and the resulting attenuation factor The ratio of the incident radiation dose or dose rate to the radiation dose or dose rate transmitted through a shielding material. This is the reciprocal of the transmission factor.  is usually computed as the ratio of the true variance In statistics, the term true variance is often used to refer to the unobservable variance of a whole finite population, as distinguished from an observable statistic based on a sample.  to the overall variance (including measurement error). In our case, the high model [R.sup.2] for the personal-ambient particle associations suggests a modest attenuation Loss of signal power in a transmission.
Attenuation

The reduction in level of a transmitted quantity as a function of a parameter, usually distance. It is applied mainly to acoustic or electromagnetic waves and is expressed as the ratio of power densities.
 of the particle associations with health in time-series studies.

Consistent with previous findings (Brauer et al. 1989; Linaker et al. 2000; Liu et al. 1997; Patterson and Eatough 2000; Sarnat et al. 2001), associations between ambient concentrations and personal exposures for [O.sub.3] and S[O.sub.2] in both seasons and for N[O.sub.2] in the summer were weak, with low slopes and [R.sup.2] values. Although, in contrast to the previous studies, our associations are statistically significant, the low slopes and [R.sup.2] values suggest that ambient gas concentrations are not suitable proxies of their respective personal exposures in time-series health studies. An exception to this was ambient N[O.sub.2] concentrations in the fall, for which the observed moderate personal--ambient association supported its ability to reflect its corresponding exposures in the fall. Significant associations in Steubenville compared with those in other studies may be due in part to differences in study design because we collected a greater number of samples and measured personal gaseous gas·e·ous
adj.
1. Of, relating to, or existing as a gas.

2. Full of or containing gas; gassy.
 exposures with greater sensitivity in Steubenville than in previous studies. Thus, we may have had greater power to detect associations between ambient and personal gas concentrations. Our present results support this theory because personal-ambient gas associations were stronger in the fall when field LODs were lower compared with those in the summer.

As was the case for particles, we found that home ventilation was an important modifier of the association between ambient concentrations and personal exposures for the gases. Personal-ambient gas associations, in particular for [O.sub.3], were highest for subjects spending time indoors where windows were open compared with those for subjects spending time indoors where windows were closed. Although the ability of ventilation to modify associations between personal and ambient gas concentrations has not been examined previously, results from a recent study of 43 children and healthy senior citizens in Boston, Massachusetts “Boston” redirects here. For other uses, see Boston (disambiguation).
Boston is the capital and most populous city of Massachusetts.[3] The largest city in New England, Boston is considered the unofficial economic and cultural center of the entire New
, provide support for our findings: Sarnat et al. (2005) found significant personal-ambient associations for [O.sub.3] and N[O.sub.2] in the summer but not the winter, possibly because of greater home ventilation in the summer in Boston. Similarly, Gold et al. (1996) showed ventilation to be an important modifier of indoor [O.sub.3] levels in an indoor--outdoor monitoring study in Mexico City Mexico City
 Spanish Ciudad de México

City (pop., 2000: city, 8,605,239; 2003 metro. area est., 18,660,000), capital of Mexico. Located at an elevation of 7,350 ft (2,240 m), it is officially coterminous with the Federal District, which occupies 571 sq mi
.

In cross-pollutant analyses, we found significant associations between ambient particle concentrations and personal [O.sub.3] exposures in both seasons and N[O.sub.2] exposures in the fall. Although significant, however, PM concentrations explained little variation in personal exposures to these gaseous pollutants. We found personal [O.sub.3] and N[O.sub.2] exposures to increase by only 1.1 and 1.7 ppb with every 10 [micro]g/[m.sup.3] increase in ambient [PM.sub.2.5], respectively. These observed increases in personal [O.sub.3] and N[O.sub.2] are extremely small and have not been shown to elicit e·lic·it  
tr.v. e·lic·it·ed, e·lic·it·ing, e·lic·its
1.
a. To bring or draw out (something latent); educe.

b. To arrive at (a truth, for example) by logic.

2.
 adverse health effects in controlled laboratory studies (Devlin et al. 1997; Frampton et al. 1991; Gong et al. 1998).

In reverse cross-pollutant models, ambient [O.sub.3] and S[O.sub.2] concentrations in both seasons and N[O.sub.2] concentrations in the summer were poor proxies of personal particle exposures. Although several cross-pollutant associations were significant for ambient [O.sub.3] and S[O.sub.2], they showed relatively low slopes and [R.sup.2] values. For most cases, the results suggest that ambient gas concentrations, although not suitable proxies of gas exposures, are equally not suitable for particle exposures in time-series health studies. Despite this, numerous epidemiologic studies have linked 24-hr ambient gas concentrations to adverse health impacts, suggesting that the gases may indeed elicit biologic responses alone or in combination with other pollutants, or are acting as proxies for shorter-term exposures.

In contrast to ambient [O.sub.3] and S[O.sub.2] in both seasons and ambient N[O.sub.2] in the summer, ambient N[O.sub.2] in the fall showed moderate associations with both personal particle and personal N[O.sub.2] exposures. We found [PM.sub.2.5] exposures to increase by 9.3 [micro]g/[m.sup.3] and N[O.sub.2] exposures to increase by 4.9 ppb for each 10 ppb increase in ambient N[O.sub.2]. The results suggest that for Steubenville in the fall, a season with strong associations between ambient particle and N[O.sub.2] concentrations, the separation of particle and N[O.sub.2] health effects in daily time-series studies may be difficult, and more precise exposure 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.  may be needed.

As demonstrated by our findings, it is important to acknowledge that personal--ambient relationships are greatly dependent on ambient conditions (e.g., season, meteorology meteorology, branch of science that deals with the atmosphere of a planet, particularly that of the earth, the most important application of which is the analysis and prediction of weather. ) and behavior (e.g., use of windows). However, further factors such as building design will also be extremely important. Because data in the present study are from a relatively small cohort of 15 subjects from one city, and previous studies examining similar exposure relationships were conducted in other eastern U.S. cities

(Boston and Baltimore) (Sarnat et al. 2001, 2005), further exposure assessment work, particularly in different geographic and climatic zones Noun 1. climatic zone - any of the geographical zones loosely divided according to prevailing climate and latitude
geographical zone, zone - any of the regions of the surface of the Earth loosely divided according to latitude or longitude
, is needed.

Conclusions

Results from our study suggest that ventilation may be an important modifier of the magnitude of effect in time-series health studies. In addition, our results indicate that ambient fine particle concentrations may represent exposures to fine particles but that the ability of either ambient gases or ambient fine particles to represent exposure to gases is quite small. The results suggest that time-series health studies based on 24-hr ambient concentrations may not be able to identify the effects of gases on health, and better exposure surrogates are needed.

Received 22 June 2005; accepted 15 December 2005.

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Stefanie Ebelt Sarnat, (1) Brent A. Coull, (2) Joel Schwartz, (3) Diane R. Gold, (3,4) and Helen H. Suh (3)

(1) Department of Environmental and Occupational Health, Rollins School of Public Health The Rollins School of Public Health (RSPH) is the public health school of Emory University. Founded in 1990, RSPH has more than 850 students pursuing master's degrees (MPH/MSPH) and over 100 students pursuing doctorate degrees (PhD). , Emory University Emory University (ĕm`ərē), near Atlanta, Ga.; coeducational; United Methodist; chartered as Emory College 1836, opened 1837 at Oxford. It became Emory Univ. in 1915 and in 1919 moved to Atlanta. , Atlanta, Georgia, USA; (2) Department of Biostatistics biostatistics /bio·sta·tis·tics/ (-stah-tis´tiks) biometry.

bi·o·sta·tis·tics
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The science of statistics applied to the analysis of biological or medical data.
, and (3) Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA; (4) Channing Laboratory, Department of Medicine, Brigham and Women's Hospital Brigham and Women's Hospital (BWH) is a hospital in the Longwood Area of the Boston, Massachusetts neighborhood of Mission Hill. With Massachusetts General Hospital, it is one of the two founding members of Partners HealthCare. , Harvard Medical School Harvard Medical School (HMS) is one of the graduate schools of Harvard University. It is a prestigious American medical school located in the Longwood Medical Area of the Mission Hill neighborhood of Boston, Massachusetts. , Boston, Massachusetts, USA

Address correspondence to S.E. Sarnat, Department of Environmental and Occupational Health, Rollins School of Public Health, Emory University, 1518 Clifton Rd. NE, Adanta, GA 30322 USA. Telephone: (404) 712-9636. Fax: (404) 727-8744. E-mail: sebelt@sph.emory.edu

We are grateful to all study participants and to the Steubenville field team, M. Verrier, M. Syring, A. Wheeler, A. Joshi, K. Gay, J. Slater slat·er  
n.
1. One employed to lay slate surfaces, as on roofs.

2. See pill bug.

3. See sow bug.

Noun 1.
, M. Brodsky, G. Allen, J. Vallerino, J. Sarnat, P. Koutrakis, and CONSOL Energy, Inc. Research & Development.

Funding for this study was from the National Institute of Environmental Health Sciences The National Institute of Environmental Health Sciences (NIEHS) is one of 27 Institutes and Centers of the National Institutes of Health (NIH),which is a component of the Department of Health and Human Services (DHHS). The Director of the NIEHS is Dr. David A. Schwartz.  (ES-09825), 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  (R826780-01-0, R827353-01-0), the Ohio Coal Development Office within the Ohio Air Quality Development Authority (CDO/D-98-2), the Electric Power Research Institute (EP-P4464/C2166), the American Petroleum Institute The American Petroleum Institute, commonly referred to as API, is the main U.S. trade association for the oil and natural gas industry, representing about 400 corporations involved in production, refinement, distribution, and many other aspects of the industry.  (78142), and the U.S. Department of Energy's (DOE) National Energy Technology Laboratory (DE-FC26-00NT40771).

Any opinions, findings, conclusions, or recommendations expressed herein are those of the authors and do not necessarily reflect the views of the DOE.

The authors declare they have no competing financial interests.
Table 1. Quality assurance parameters.

                                Field (LOD) (a)
                                                  Imprecision  Accuracy
Pollutant             Season   Ambient  Personal      (%)        (%)

Particles

  [PM.sub.2.5]        Summer      3.0       6.6       1-2          93
                      Fall        2.9       5.7       0-2
  S[O.sup.2-.sub.4]   Summer      0.2       0.4       10.8         NA
                      Fall        0.2       0.2
  EC                  Summer      0.55      0.55      14.5         NA
                      Fall        0.04      0.04

Gases
  [O.sub.3]           Summer     12.7      12.7       10.4         92
                      Fall       10.7      10.7
  N[O.sub.2]          Summer     10.8      10.8       17.0        106
                      Fall        6.4       6.4
  S[O.sub.2]          Summer      5.5       5.5       24.9         73
                      Fall        3.8       3.8

NA, reference measures not available for determining accuracy of
S[O.sup.2-.sub.4] and EC.

(a) LODs for particles are in units of micrograms per cubic meter;
LODS for gases, in parts per billion.

Table 2. Summary statistics of all measured concentrations. (a)

                                              Summer

Pollutant                 n    ND   LOD    Mean [+ or -] SD    Maximum

Ambient concentrations

 Particles
  [PM.sub.2.5]            65    0     0   20.1 [+ or -] 9.3       46.6
  S[O.sup.2-.sub.4]       61    0     0    7.7 [+ or -] 4.8       25.0
  EC                      56    0     1    1.1 [+ or -] 0.5        2.9
 Gases
  [O.sub.3]               62    0     4   29.3 [+ or -] 13.4      74.8
  N[O.sub.2]              62    1    44    9.5 [+ or -] 7.4       37.9
  S[O.sub.2]              63   23    53    2.7 [+ or -] 3.9       21.9

Personal exposures

 Particles
  [PM.sub.2.5]           169    0     0   19.9 [+ or -] 9.4       59.0
  S[O.sup.2-.sub.4]      165    0     2    5.9 [+ or -] 4.2       25.6
  EC                     166    7    12    1.1 [+ or -] 0.6        4.6
 Gases
  [O.sub.3]              183    2   168    5.3 [+ or -] 5.2       35.7
  N[O.sub.2]             183    1   117    9.9 [+ or -] 6.0       38.9
  N[O.sub.2] (b)         130    1    93    9.0 [+ or -] 5.2       38.9
  N[O.sub.2] (c)          53    0    24   12.3 [+ or -] 7.1       33.5
  S[O.sub.2]             185   99   173    1.5 [+ or -] 3.3       30.4

                                              Fall

Pollutant                 n    NO   LOD    Mean [+ or -] SD    Maximum

Ambient concentrations

 Particles
  [PM.sub.2.5]            72    0     0   19.3 [+ or -] 12.2      50.7
  S[O.sup.2-.sub.4]       72    0     0    6.2 [+ or -] 4.7       22.4
  EC                      71    0     0    1.1 [+ or -] 0.7        3.6
 Gases
  [O.sub.3]               72    0    21   16.0 [+ or -] 8.1       42.4
  N[O.sub.2]              71    0    16   11.3 [+ or -] 6.0       27.9
  S[O.sub.2]              71   24    43    5.4 [+ or -] 9.6       63.6

Personal exposures

 Particles
  [PM.sub.2.5]           204    0     0   20.1 [+ or -] 11.6      66.0
  S[O.sup.2-.sub.4]      188    0     0    4.4 [+ or -] 3.3       16.3
  EC                     197    1     1    1.2 [+ or -] 0.7        6.2
 Gases
  [O.sub.3]              226   84   207    3.9 [+ or -] 4.4       21.3
  N[O.sub.2]             228    1    32   12.1 [+ or -] 6.1       38.8
  N[O.sub.2] (b)         139    1    28    9.9 [+ or -] 4.6       28.7
  N[O.sub.2] (c)          89    0     4   15.7 [+ or -] 6.4       38.8
  S[O.sub.2]             228   72   217    0.7 [+ or -] 1.9       14.2

ND, number of samples with values below the analytical LOD (i.e., not
detected).

(a) [PM.sub.2.5], S[O.sup.2-.sub.4], and EC in units of micrograms per
cubic meter; [O.sub.3], N[O.sub.2], and S[O.sub.2] in units of parts
per billion. (b) Samples from subjects without gas stoves in their
homes. (c) Samples from subjects with gas stoves in their homes.

Table 3. Associations between ambient particle and gas concentrations.

                                             Summer

Model                     n    Slope [+ or -] SE     t-Value  [R.sup.2]

Ambient [0.sub.3] =       62   0.74 [+ or -] 0.16 *     4.55    0.26
  ambient [PM.sub.2.5]
Ambient N[0.sub.2] =      62  -0.01 [+ or -] 0.11      -0.10    0.00
  ambient [PM.sub.2.5]
Ambient S[0.sub.2] =      63   0.07 [+ or -] 0.05       1.37    0.03
  ambient [PM.sub.2.5]
Ambient [0.sub.3] =       58   1.45 [+ or -] 0.28 *     5.09    0.27
  ambient
  S[O.sup.2-.sub.4]
Ambient N[0.sub.2] =      58  -0.17 [+ or -] 0.21      -0.79    0.01
  ambient
  S[O.sup.2-.sub.4]
Ambient S[0.sub.2] =      59   0.18 [+ or -] 0.11       1.66    0.05
  ambient
  S[O.sup.2-.sub.4]
Ambient [0.sub.3] =       53  -6.98 [+ or -] 3.90      -1.79    0.06
  ambient EC
Ambient N[0.sub.2] =      53   3.76 [+ or -] 2.19       1.72    0.06
  ambient EC
Ambient S[0.sub.2] =      54  -0.65 [+ or -] 0.81      -0.80    0.01
  ambient EC

                                             Fall

Model                     n    Slope [+ or -] SE     t-Value  [R.sup.2]

Ambient [0.sub.3] =       72  -0.20 [+ or -] 0.08 *    -2.41    0.07
  ambient [PM.sub.2.5]
Ambient N[0.sub.2] =      71   0.38 [+ or -] 0.04 *     9.75    0.61
  ambient [PM.sub.2.5]
Ambient S[0.sub.2] =      71   0.40 [+ or -] 0.10 *     4.14    0.22
  ambient [PM.sub.2.5]
Ambient [0.sub.3] =       72  -0.52 [+ or -] 0.23 *    -2.24    0.07
  ambient
  S[O.sup.2-.sub.4]
Ambient N[0.sub.2] =      71   0.96 [+ or -] 0.12 *     7.90    0.49
  ambient
  S[O.sup.2-.sub.4]
Ambient S[0.sub.2] =      71   1.38 [+ or -] 0.25 *     5.45    0.33
  ambient
  S[O.sup.2-.sub.4]
Ambient [0.sub.3] =       71  -3.18 [+ or -] 1.44 *    -2.20    0.06
  ambient EC
Ambient N[0.sub.2] =      70   7.01 [+ or -] 0.62 *    11.39    0.68
  ambient EC
Ambient S[0.sub.2] =      70   9.39 [+ or -] 1.56 *     6.03    0.34
  ambient EC

* Slope significant at the 0.05 level.

Table 4. Personal-ambient pollutant associations.

                                              Summer

Model                      n    Slope [+ or -] SE    t-Value  [R.sup.2]

Particles
  Personal [PM.sub.2.5]   167  0.73 [+ or -] 0.05 *    16.08    0.60
    = ambient
    [PM.sub.2.5]
  Personal                150  0.74 [+ or -] 0.02 *    32.35    0.88
    S[0.sup.2.sub.4]
    = ambient
    S[0.sup.2.sub.4]
  Personal EC =           142  0.33 [+ or -] 0.10 *     3.24    0.08
    ambient EC

Gases
  Personal [O.sub.3] =    174  0.15 [+ or -] 0.02 *     7.21    0.24
    ambient [O.sub.3]
  Personal                122  0.25 [+ or -] 0.06 *     4.30    0.14
    N[O.sub.2] (a) =
    ambient N[O.sub.2]
  Personal S[O.sub.2]     106  0.03 [+ or -] 0.10       0.29    0.00
    = ambient
    S[O.sub.2] (b)

                                              Fall

Model                      n    Slope [+ or -] SE    t-Value  [R.sup.2]

Particles
  Personal [PM.sub.2.5]   204  0.63 [+ or -] 0.05 *    13.32    0.47
    = ambient
    [PM.sub.2.5]
  Personal                188  0.64 [+ or -] 0.02 *    26.36    0.80
    S[0.sup.2.sub.4]
    = ambient
    S[0.sup.2.sub.4]
  Personal EC =           193  0.70 [+ or -] 0.06 *    12.43    0.44
    ambient EC

Gases
  Personal [O.sub.3] =    226  0.27 [+ or -] 0.03 *     8.64    0.25
    ambient [O.sub.3]
  Personal                138  0.49 [+ or -] 0.05 *    10.09    0.43
    N[O.sub.2] (a) =
    ambient N[O.sub.2]
  Personal S[O.sub.2]     152  0.08 [+ or -] 0.02 *     4.98    0.15
    = ambient
    S[O.sub.2] (b)

(a) Models predicting personal N[O.sub.2] exposures restricted to
subjects residing in homes without gas stoves. (b) Models using ambient
S[O.sub.2] as the independent variable restricted to data greater than
the analytical LOD. * Slope significant atthe 0.05 level.

Table 5. Personal-ambient associations by ventilation status.

                                              Summer

Model                            Vent   n       Slope [+ or -] SE

Particles
  Personal [PM.sub.2.5] =        Low    32   0.59 [+ or -] 0.12 *
    ambient [PM.sub.2.5]         High  133   0.76 [+ or -] 0.05 *
  Personal S[O.sup.2-.sub.4] =   Low    25   0.51 [+ or -] 0.06 *, (#)
    ambient S[O.sup.2-.sub.4]    High  123   0.77 [+ or -] 0.02 *, (#)
  Personal EC = ambient EC       Low    25   0.13 [+ or -] 0.19
                                 High  116   0.41 [+ or -] 0.12 *
Gases
  Personal [O.sub.3] =           Low    34   0.08 [+ or -] 0.04 (#)
    ambient [O.sub.3]            High  138   0.18 [+ or -] 0.03 *, (#)
  Personal N[O.sub.2] (b) =      Low    30   0.24 [+ or -] 0.11 *
    ambient N[O.sub.2]           High   90   0.27 [+ or -] 0.07 *
  Personal S[O.sub.2] =          Low    21   0.07 [+ or -] 0.15
    ambient S[O.sub.2] (c)       High   84  -0.06 [+ or -] 0.15

                                               Summer

Model                            Vent       t-Value     [R.sup.2] (a)

Particles
  Personal [PM.sub.2.5] =        Low          5.14          0.46
    ambient [PM.sub.2.5]         High        15.39          0.64
  Personal S[O.sup.2-.sub.4] =   Low          8.32          0.81
    ambient S[O.sup.2-.sub.4]    High        32.81          0.90
  Personal EC = ambient EC       Low          0.69          0.05
                                 High         3.40          0.10
Gases
  Personal [O.sub.3] =           Low          1.89          0.19
    ambient [O.sub.3]            High         7.34          0.27
  Personal N[O.sub.2] (b) =      Low          2.26          0.34
    ambient N[O.sub.2]           High         3.88          0.16
  Personal S[O.sub.2] =          Low          0.46          0.04
    ambient S[O.sub.2] (c)       High        -0.39          0.00

                                              Fall

Model                            Vent    n        Slope [+ or -] SE

Particles
  Personal [PM.sub.2.5] =        Low     97   0.53 [+ or -] 0.07 *
    ambient [PM.sub.2.5]         High   107   0.65 [+ or -] 0.06 *
  Personal S[O.sup.2-.sub.4] =   Low     87   0.57 [+ or -] 0.04 *
    ambient S[O.sup.2-.sub.4]    High   101   0.67 [+ or -] 0.03 *
  Personal EC = ambient EC       Low     95   0.66 [+ or -] 0.08 *
                                 High    98   0.73 [+ or -] 0.09 *
Gases
  Personal [O.sub.3] =           Low    109   0.20 [+ or -] 0.05 *
    ambient [O.sub.3]            High   117   0.27 [+ or -] 0.04 *
  Personal N[O.sub.2] (b) =      Low     79   0.44 [+ or -] 0.07 *
    ambient N[O.sub.2]           High    59   0.46 [+ or -] 0.07 *
  Personal S[O.sub.2] =          Low     83   0.07 [+ or -] 0.02 *
    ambient S[O.sub.2] (c)       High    69   0.13 [+ or -] 0.04 *

                                                Fall

Model                            Vent     t-Value    [R.sup.2] (a)

Particles
  Personal [PM.sub.2.5] =        Low        7.22        0.35
    ambient [PM.sub.2.5]         High      10.14        0.53
  Personal S[O.sup.2-.sub.4] =   Low       14.86        0.76
    ambient S[O.sup.2-.sub.4]    High      21.31        0.82
  Personal EC = ambient EC       Low        8.61        0.38
                                 High       8.60        0.53
Gases
  Personal [O.sub.3] =           Low        3.90        0.12
    ambient [O.sub.3]            High       7.38        0.33
  Personal N[O.sub.2] (b) =      Low        6.83        0.47
    ambient N[O.sub.2]           High       6.15        0.34
  Personal S[O.sub.2] =          Low        3.90        0.13
    ambient S[O.sub.2] (c)       High       3.15        0.20

Vent, ventilation status: low = subjects spending no time indoors with
open windows; high = subjects spending any time indoors with open
windows.

(a) [R.sup.2] values estimated using results of models stratified by
ventilation status as opposed to models incorporating an interaction
term. (b) Models predicting personal N[O.sub.2] exposures restricted
to subjects residing in homes without gas stoves. (c) Models using
ambient S[O.sub.2] as the independent variable restricted to data
greater than the analytical LOD. * Slope significant at the 0.05
level. (#) Significant difference in slopes between levels of
ventilation status.

Table 6. Associations between ambient particle concentrations and
personal gas exposures.

                                        Summer

Model                            n      Slope [+ or -] SE

Personal [O.sub.3] =            181      0.11 [+ or -] 0.03 *
  ambient [PM.sub.2.5]
Personal N[O.sub.2] (a)         128     -0.01 [+ or -] 0.05
  = ambient [PM.sub.2.5]
Personal S[O.sub.2] =           183   -0.0004 [+ or -] 0.03
  ambient [PM.sub.2.5]
Personal [0.sub.3] =            168      0.16 [+ or -] 0.06 *
  ambient
  S[O.sup.2-.sub.4]
Personal N[O.sub.2] (a)         118     -0.09 [+ or -] 0.10
  = ambient
  S[O.sup.2-.sub.4]
Personal S[O.sub.2] =           169     -0.06 [+ or -] 0.05
  ambient
  S[O.sup.2-.sub.4]
Personal [O.sub.03] =           154     -0.81 [+ or -] 0.64
  ambient EC
Personal N[O.sub.2] (a)         107      1.81 [+ or -] 0.91 *
  = ambient EC
Personal S[O.sub.2] =           157      0.59 [+ or -] 0.52
  ambient EC

                                     Summer

Model                        t-Value       [R.sup.2]

Personal [O.sub.3] =           3.46           0.06
  ambient [PM.sub.2.5]
Personal N[O.sub.2] (a)       -0.24           0.00
  = ambient [PM.sub.2.5]
Personal S[O.sub.2] =         -0.02           0.00
  ambient [PM.sub.2.5]
Personal [0.sub.3] =           2.58           0.04
  ambient
  S[O.sup.2-.sub.4]
Personal N[O.sub.2] (a)       -0.86           0.01
  = ambient
  S[O.sup.2-.sub.4]
Personal S[O.sub.2] =         -1.22           0.01
  ambient
  S[O.sup.2-.sub.4]
Personal [O.sub.03] =         -1.28           0.01
  ambient EC
Personal N[O.sub.2] (a)        1.99           0.03
  = ambient EC
Personal S[O.sub.2] =          1.14           0.01
  ambient EC

                                     Fall

Model                            n       Slope [+ or -] SE

Personal [O.sub.3] =            226     0.10 [+ or -] 0.02 *
  ambient [PM.sub.2.5]
Personal N[O.sub.2] (a)         139     0.17 [+ or -] 0.03 *
  = ambient [PM.sub.2.5]
Personal S[O.sub.2] =           228   0.0005 [+ or -] 0.01
  ambient [PM.sub.2.5]
Personal [0.sub.3] =            226     0.27 [+ or -] 0.06 *
  ambient
  S[O.sup.2-.sub.4]
Personal N[O.sub.2] (a)         139     0.34 [+ or -] 0.08 *
  = ambient
  S[O.sup.2-.sub.4]
Personal S[O.sub.2] =           228    0.007 [+ or -] 0.03
  ambient
  S[O.sup.2-.sub.4]
Personal [O.sub.03] =           222     1.27 [+ or -] 0.44 *
  ambient EC
Personal N[O.sub.2] (a)         136     3.71 [+ or -] 0.51 *
  = ambient EC
Personal S[O.sub.2] =           224    -0.11 [+ or -] 0.20
  ambient EC

                                     Fall

Model                         t-Value      [R.sup.2]

Personal [O.sub.3] =           4.24           0.07
  ambient [PM.sub.2.5]
Personal N[O.sub.2] (a)        5.82           0.21
  = ambient [PM.sub.2.5]
Personal S[O.sub.2] =          0.05           0.00
  ambient [PM.sub.2.5]
Personal [0.sub.3] =           4.42           0.08
  ambient
  S[O.sup.2-.sub.4]
Personal N[O.sub.2] (a)        4.14           0.12
  = ambient
  S[O.sup.2-.sub.4]
Personal S[O.sub.2] =          0.27           0.00
  ambient
  S[O.sup.2-.sub.4]
Personal [O.sub.03] =          2.92           0.04
  ambient EC
Personal N[O.sub.2] (a)        7.32           0.32
  = ambient EC
Personal S[O.sub.2] =         -0.57           0.00
  ambient EC

(a) Models predicting personal N[O.sub.2] exposures restricted to
subjects residing in homes without gas stoves. * Slope significant
at the 0.05 level.

Table 7. Associations between ambient gas concentrations and personal
particle exposures.

                                            Summer

Model                    n     Slope [+ or -] SE     t-Value  [R.sup.2]

Personal [PM.sub.2.5]   159   0.28 [+ or -] 0.05 *      5.46    0.16
  = ambient [O.sub.3]
Personal [PM.sub.2.5]   159  -0.07 [+ or -] 0.09       -0.80    0.00
  = ambient N[O.sub.2]
Personal [PM.sub.2.5]    95   0.73 [+ or -] 0.27 *      2.70    0.07
  = ambient
  S[O.sub.2] (a)
Personal                155   0.14 [+ or -] 0.02 *      5.56    0.16
  S[O.sup.2-.sub.4] =
  ambient [O.sub.3]
Personal                155  -0.06 [+ or -] 0.04       -1.55    0.01
  S[O.sup.2-.sub.4] =
  ambient N[O.sub.2]
Personal                 93   0.21 [+ or -] 0.12        1.70    0.03
  S[O.sup.2-.sub.4] =
  ambient
  S[O.sub.2] (a)
Personal EC = ambient   157  -0.01 [+ or -] 0.004 *    -2.60    0.04
  [O.sub.3]
Personal EC = ambient   157   0.02 [+ or -] 0.006 *     3.45    0.07
  N[O.sub.2]
Personal EC = ambient    92   0.02 [+ or -] 0.02        0.88    0.01
  S[O.sub.2] (a)

                                             Fall

Model                    n     Slope [+ or -] SE     t-Value  [R.sup.2]

Personal [PM.sub.2.5]   204   0.08 [+ or -] 0.10        0.78    0.00
  = ambient [O.sub.3]
Personal [PM.sub.2.5]   203   0.93 [+ or -] 0.11 *      8.25    0.25
  = ambient N[O.sub.2]
Personal [PM.sub.2.5]   136   0.18 [+ or -] 0.11        1.60    0.02
  = ambient
  S[O.sub.2] (a)
Personal                188   0.01 [+ or -] 0.03        0.49    0.00
  S[O.sup.2-.sub.4] =
  ambient [O.sub.3]
Personal                187   0.28 [+ or -] 0.04 *      7.78    0.27
  S[O.sup.2-.sub.4] =
  ambient N[O.sub.2]
Personal                125   0.07 [+ or -] 0.03 *      2.48    0.06
  S[O.sup.2-.sub.4] =
  ambient
  S[O.sub.2] (a)
Personal EC = ambient   197  -0.02 [+ or -] 0.006 *    -3.00    0.04
  [O.sub.3]
Personal EC = ambient   196   0.08 [+ or -] 0.006 *    13.60    0.49
  N[O.sub.2]
Personal EC = ambient   135   0.02 [+ or -] 0.008 *     2.47    0.05
  S[O.sub.2] (a)

(a) Models using ambient S[O.sub.2] as the independent variable
restricted to data greater than the analytical LOD. * Slope significant
at the 0.05 level.

Table 8. Tobit model results for personal--ambient associations
predicting personal [O.sub.3] and S[O.sub.2] exposures. (a)

                                             Summer

Model                            n      Slope [+ or -] SE     t-Value

Models as in Table 4
  Personal [O.sub.3] =
    ambient [O.sub.3]
  Personal S[O.sub.2] =         106    0.08 [+ or -] 0.15        0.53
    ambient S[O.sub.2]
Models as in Table 6
  Personal [O.sub.3] =
    ambient [PM.sub.2.5]
  Personal S[O.sub.2] =         184    0.05 [+ or -] 0.05        1.09
    ambient [PM.sub.2.5]
  Personal [O.sub.3] =
    ambient S[O.sub.2-.sub.4]
  Personal S[O.sub.2] =         170   -0.05 [+ or -] 0.11       -0.45
    ambient S[O.sub.2-.sub.4]
  Personal [O.sub.3] =
    ambient EC
  Personal S[O.sub.2] =         158    1.23 [+ or -] 1.02        1.21
    ambient EC

                                              Fall

Model                            n    Slope [+ or -] SE       t-Value

Models as in Table 4
  Personal [O.sub.3] =          226    0.30 [+ or -] 0.04 *      8.59
    ambient [O.sub.3]
  Personal S[O.sub.2] =         152    0.08 [+ or -] 0.02 *      4.16
    ambient S[O.sub.2]
Models as in Table 6
  Personal [O.sub.3] =          226    0.12 [+ or -] 0.03 *      4.42
    ambient [PM.sub.2.5]
  Personal S[O.sub.2] =         228   -0.02 [+ or -] 0.01       -1.29
    ambient [PM.sub.2.5]
  Personal [O.sub.3] =          226    0.32 [+ or -] 0.07 *      4.68
    ambient S[O.sub.2-.sub.4]
  Personal S[O.sub.2] =         228   -0.05 [+ or -] 0.04       -1.41
    ambient S[O.sub.2-.sub.4]
  Personal [O.sub.3] =          222    1.56 [+ or -] 0.51 *      3.05
    ambient EC
  Personal S[O.sub.2] =         224   -0.46 [+ or -] 0.25       -1.80
    ambient EC

(a) Tobit models used for predicting exposures with extreme proportion
(i.e., > 30%) of nondetect samples (i.e., [O.sub.3] exposures in the
fall only and S[O.sub.2] exposures in both seasons). * Slope
significant at the 0.05 level.
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Title Annotation:Research
Author:Suh, Helen H.
Publication:Environmental Health Perspectives
Date:May 1, 2006
Words:9626
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