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Final Report: Personal PM Exposure Assessment
EPA Grant Number: R827355C003Subproject: this is subproject number 003 , established and managed by the Center Director under grant R827355
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
Center: Airborne PM - Northwest Research Center for Particulate Air Pollution and Health
Center Director: Koenig, Jane Q.
Title: Personal PM Exposure Assessment
Investigators: Liu, Sally , Allen, Ryan , Claiborn, Candis , Kalman, Dave , Koenig, Jane Q. , Larson, Timothy V. , Simpson, Chris
Institution: University of Washington
EPA Project Officer: Stacey Katz/Gail Robarge,
Project Period: June 1, 1999 through May 31, 2004 (Extended to May 31, 2006)
Project Amount: Refer to main center abstract for funding details.
RFA: Airborne Particulate Matter (PM) Centers (1999)
Research Category: Particulate Matter
Description:
Objective:- Characterize and compare personal, indoor, and outdoor exposures to the various components of PM10 and PM2.5 among three major susceptible subpopulations and one control healthy cohort;
- Determine the strength of the relationship of the particle exposures of the high-risk subpopulations to the concentrations measured by a central monitoring station;
- Characterize the key factors influencing this relationship and develop models for predicting personal PM exposures;
- Provide exposure models for the concurrent epidemiologic study to reach unbiased estimation of health effects; and
- Update the study design and analysis goals based on the new findings and new hypotheses
A large panel study of Seattle residents (Liu, et. al., 2003) has provided important information about daily average exposures of at-risk groups to PM2.5 and also about the relationship between personal exposures to PM2.5 and simultaneous measurements at fixed-site community monitors. This 3-year study involved 108 individuals with and without chronic obstructive pulmonary disease (COPD), coronary heart disease (CHD), and asthma. Overall, the elderly healthy, COPD and CHD subjects had similar exposures. All three groups had lower exposures than the asthmatic children. Within a given group, the PM2.5 exposure varied with the subject’s residences due to the different particle infiltration efficiencies of different buildings. Although a wide range of longitudinal correlations between central site and personal PM2.5 measurements were found, the longitudinal correlation for any given subject was closely related to the particle infiltration efficiency, Finf, of their residence.
Allen et.al. (2003) used a recursive mass balance model to estimate the 10-day average Finf for individual residences of panel subjects. The average Finf was 0.65 ± 0.21 for 44 residences. Finf differed significantly by season, being higher in the non-heating season. For the 44 study residences, outdoor-generated particles accounted for an average of 79 ± 17% of the indoor PM concentration, with a range of 40 to 100% at individual residences. In a subsequent analysis of 62 residences of panel subjects, Finf was found to vary by residence type (group homes > private residences), the presence of an air cleaner, and meteorological conditions (temperature and rainfall) (Koenig et. al., 2005).
The Seattle panel study also served as a basis to estimate daily personal exposures to PM2.5 of both indoor and outdoor origin. This work is based on the following conceptual model:
ET = Eag + Eig + ‘personal cloud’
Eag = (F0)C0+ (1-F0)(C0) (Finf)
Eig = (1-F0)[Ci – (C0) (Finf)]
ET is the individual personal exposure to PM2.5, Eag is the individual personal exposure to PM2.5 of outdoor origin, Eig is the exposure to PM2.5 of indoor origin, C0 and Ci are the outdoor and indoor PM2.5 concentrations, respectively, and F0 is the fraction of time that subjects are outdoors. We can also define an ambient PM2.5 contribution fraction, α = Eag/C0. This approach typically measures C0 and Ci using fixed-site monitors and attributes any additional exposures not captured by these measurements to the ‘personal cloud’. The validity of this latter assumption can be investigated by using hourly exposure measurements and corresponding hourly activity information. Allen et.al. (2004) employed continuous personal light scattering measurements to estimate Eag and Eig for PM2.5 among 38 of the panel study subjects. Previously published estimates (Allen et.al, 2003) of particle infiltration (Finf) were combined with hourly time-location data to estimate a (mean = 0.66 ± 0.21) for each subject. The mean a was lower for subjects monitored during the heating season (0.55 ± 0.16) than during the non-heating season (0.80 ± 0.17). On average, ambient particles accounted for 48% of total personal exposure (range = 21-80%). The personal activity exposure was highly influenced by time spent away from monitored microenvironments. Total personal exposure was poorly predicted by stationary outdoor monitors among persons whose PM exposure was dominated by non-ambient exposures, for example, those living in tightly sealed homes, those who cook, and active children. Using a similar approach, Wu et al (2004, 2005) studied children living in Alpine, CA. The contributions to the children’s hourly, personal PM2.5 exposure from outdoor sources, indoor sources and personal activity were 11.1, 5.5, and 10.0 μg/m3, respectively, when the modeling error was minimized. The high PM2.5 exposure to personal activities reported in this study was attributed to the children's more active lifestyle as compared with older adult subjects in previous studies.
Sheppard et al. (2005, 2005a) conducted a series of simulated acute health effects studies to examine the consequences of using the ambient concentration measured at fixed sites in place of personal exposures measurements. In their simulations, they included important model parameters based upon the Seattle panel study results (Liu, et.al., 2003; Allen et.al., 2003, 2004). These parameters included the distributions of C0, Ci, α, and Eig. Assuming that a does not vary with individual over time, they found no noticeable impact on the estimation of the effect estimate from the time-series model, even under the most restrictive condition that Eig is independent of Eag. However, when the value of a for each individual was allowed to vary with season, the time series health effect estimates changed. They concluded that understanding the temporal variability in a is important to interpreting the results of time series studies. They also concluded that the suggestion of using total personal exposure as the exposure metric of interest for acute time-series studies (NRC, 1998; Zeger et al, 2000) is not realistic because daily personal measurements are needed for the entire study population. Using only a few individuals to estimate the daily average population exposure results in a highly attenuated health effect estimate but it is possible to correct for this measurement bias using a measurement error model.
In addition to particle mass, as subset of the Seattle panel filters were analyzed for selected chemical species and positive matrix factorization was then used to identify five contributing sources: vegetative burning, mobile emissions, secondary sulfate, a source rich in chlorine, and a source of crustal-derived material. Vegetative burning contributed the majority of mass and black carbon in all samples. The indoor/outdoor ratios for vegetative burning and secondary source contributions varied significantly by residence, in agreement with the infiltration efficiencies derived using the recursive mass balance model approach (Allen et.al, 2003). Personal exposure to the combustion-derived particles was correlated with outdoor sources whereas exposure to the crustal and Cl-rich particles was not. Personal exposures to crustal sources were strongly associated with personal activities, especially time spent at school among the children. These results are in agreement with a follow-up panel in Seattle (Jansen et al, 2005) that measured indoor, outdoor and personal levels of black carbon on a daily basis in adult subjects with asthma and/or COPD. They found good correlations between daily measures of indoor, outdoor and personal BC, but poor correlations between outdoor and personal PM10.
Exposure and health assessments of the effects of agricultural field burning in young adults with asthma living in Pullman Washington
Agricultural (Ag) burning is a cost-effective method of cleaning and preparing the field for the succeeding growth season. However, smoke from Ag burning may contain various air pollutants, which may cause or exacerbate respiratory disease. However, the short-duration excursions of Ag burning smoke often do not violate the National Ambient Air Quality Standards at locations where air quality monitors are situated. Although a limited number of studies documented potential health effects from Ag burning smoke, there is a paucity of literature characterizing community residents’ exposure to Ag burning smoke and the associated health effects.
A study was conducted Pullman, Washington to evaluate the PM exposures and health effects of agricultural field burning in young adults with asthma. We hypothesized that adults with mild-moderate asthma who are not using anti-inflammatory medication would show a positive association of exhaled nitric oxide (FENO) and negative association of FEV1 and maximal mid-expiratory flow (MMEF) with the peak 1-hour average of PM2.5 during the previous 24 hours. During the study period, the observed 1-hour average PM2.5 concentrations ranged between 0.3 and 59.6 μg/m3, averaging 13.0±9.2 μg/m3. There was no significant effect of peak 1-hour PM2.5 on measures of FENO among those not prescribed anti-inflammatory medications: -0.35 ppb (95% CI: -1.70, 1.01) per 10 μg/m3 increase in PM2.5 or those prescribed controller medications: 1.68 ppb (95% CI: -1.51, 4.87) per 10 μg/m3 increase of PM2.5. Similar null effects of peak PM2.5 exposure were noted for spirometric measures of MMEF and FEV1. In conclusion, the observed PM2.5 levels and excursions were typical of those of previous years. Although we did not find an association between peak PM2.5 from field burning and decrements in pulmonary function or increases in FENO in young adults with asthma, we cannot rule-out health effects from field burning in more susceptible populations or at higher PM concentrations.
Children’s Exposure to Diesel Exhaust while Riding School Buses with Different Diesel Engines – A Pilot Study
Lee Jane Sally Liu, ScD (PI), Tim Gould, MS, Teal Hallstrand, MD, MPH, Michael Compher, MS
The objective of this pilot study was to test the feasibility of monitoring PM during school bus trips, recruiting children with asthma to participate in such a study, and to examine the feasibility of measuring lung function and exhaled breath in the field in these children.
In this pilot study we assessed the exposures of nine asthmatic and non-asthmatic children in Seattle while they rode to and from school in a variety of makes and models of diesel school buses, including two retrofitted with an oxidative catalyst to reduce emissions. Using validated portable instruments, on-bus exposures to fine and ultrafine particles, elemental carbon and organic carbon, sulfur dioxide, and nitrogen dioxide were quantified during subjects’ commutes. We also measured personal exposure including real-time PM2.5 and integrated concentrations of SO2, NO2, and PM2.5, on each subject. We performed health measures immediately after the morning commute and before the afternoon ride to explore respiratory health effects including lung function, the fractional content of nitric oxide in exhaled breath (FENO), and oxidative stress makers in exhaled breath condensate (eBC). The commute to and from school on the bus may be the most substantial source of PM2.5 exposure that children regularly encounter during their daily activities. In-vehicle exposure to PM2.5 averaged 60 μg/m3 and was seven times higher than the concurrent ambient levels. Ultrafine particle counts on the buses averaged 67,727 particle counts/cm3, 2-fold greater than the concentrations of ultafine particle measured at 60 μg/m3 PM2.5 in controlled chamber tests during equipment validation. For non-asthmatic children (n=4) and the asthmatic child not on medication, the FENO and H2O2 in eBC showed positive correlations with exposure to ultrafine particle counts during the morning commute. Non-asthmatic children (n=5) also showed significant decrements in FEV1 related to ultrafine PM during the morning commute. Our pilot study results suggest that health effects of PM and diesel exhaust are related to the magnitude of acute exposures, with non-asthmatic children being more responsive to diesel exhaust exposure than asthmatic children on anti-inflammatory medicine.
Journal Articles on this Report: 24 Displayed | Download in RIS Format
| Other subproject views: | All 65 publications | 25 publications in selected types | All 25 journal articles |
| Other center views: | All 191 publications | 97 publications in selected types | All 94 journal articles |
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Allen R, Box M, Liu L-JS, Larson TV. A cost-effective weighing chamber for particulate matter filters. Journal of the Air & Waste Management Association 2001;51(12):1650-1653. |
R827355 (2001) R827355 (Final) R827355C003 (2001) R827355C003 (Final) R827355C008 (Final) |
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Allen R, Larson T, Sheppard L, Wallace L, Liu L-JS. Use of real-time light scattering data to estimate the contribution of infiltrated and indoor-generated particles to indoor air. Environmental Science & Technology 2003;37(16):3484-3492. |
R827355 (2004) R827355 (Final) R827355C003 (2003) R827355C003 (Final) R827355C008 (Final) R827355C009 (2003) |
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Allen R, Wallace L, Larson T, Sheppard L, Liu L-JS. Estimated hourly personal exposures to ambient and nonambient particulate matter among sensitive populations in Seattle, Washington. Journal of the Air & Waste Management Association 2004;54(9):1197-1211. |
R827355 (2004) R827355 (Final) R827355C003 (2003) R827355C003 (2004) R827355C003 (Final) R827355C008 (Final) R827355C009 (Final) |
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Allen R, Wallace L, Sheppard L, Larson T, Liu L-JS. Evaluation of the recursive model approach for estimating particulate matter infiltration efficiencies using continuous light scattering data. Journal of Exposure Science & Environmental Epidemiology 2007;17(5):468-477. |
R827355 (Final) R827355C003 (Final) |
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Bayer-Oglesby L, Schindler C, Hazenkamp-von Arx ME, Braun-Fahrländer C, Keidel D, Rapp R, Künzli N, Braendli O, Burdet L, Liu L-JS, Leuenberge P, Ackermann-Liebrich U, SAPALDIA Team. Living near main streets and respiratory symptoms in adults; the Swiss Cohort Study on Air Pollution and Lung Diseases in Adults. American Journal of Epidemiology 2006;164(12):1190-1198. |
R827355 (Final) R827355C003 (Final) |
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Delfino RJ, Quintana PJE, Floro J, Gastañaga VM, Samimi BS, Kleinman MT, Liu L-JS, Bufalino C, Wu C-F, McLaren CE. Association of FEV1 in asthmatic children with personal and microenvironmental exposure to airborne particulate matter. Environmental Health Perspectives 2004;112(8):932-941. |
R827355 (2004) R827355 (Final) R827355C003 (2003) R827355C003 (2004) R827355C003 (Final) |
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Dhammapala R, Claiborn C, Corkill J, Gullett B. Particulate emissions from wheat and Kentucky bluegrass stubble burning in eastern Washington and northern Idaho. Atmospheric Environment 2006;40(6):1007-1015. |
R827355 (Final) R827355C003 (Final) |
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Goswami E, Larson T, Lumley T, Liu L-JS. Spatial characteristics of fine particulate matter: identifying representative monitoring locations in Seattle, Washington. Journal of the Air & Waste Management Association 2002;52(3):324-333. |
R827355 (2004) R827355 (Final) R827355C003 (2001) R827355C003 (Final) |
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Jimenez J, Wu C-F, Claiborn C, Gould T, Simpson CD, Larson T, Liu L-JS. Agricultural burning smoke in eastern Washington—Part I: Atmospheric characterization. Atmospheric Environment 2006;40(4):639-650. |
R827355 (Final) R827355C003 (2004) R827355C003 (Final) R827355C010 (Final) |
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Koenig JQ, Jansen K, Mar TF, Lumley T, Kaufman J, Trenga CA, Sullivan J, Liu L-JS, Shapiro GG, Larson TV. Measurement of offline exhaled nitric oxide in a study of community exposure to air pollution. Environmental Health Perspectives 2003;(13)111:1625-1629. |
R827355 (2004) R827355 (Final) R827355C002 (2002) R827355C002 (Final) R827355C003 (Final) R827355C009 (2003) |
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Koenig JQ, Mar TF, Allen RW, Jansen K, Lumley T, Sullivan JH, Trenga CA, Larson TV, Liu L-JS. Pulmonary effects of indoor-and outdoor-generated particles in children with asthma. Environmental Health Perspectives 2005;113(4):499-503. |
R827355 (2004) R827355 (Final) R827355C002 (2003) R827355C002 (2004) R827355C002 (Final) R827355C003 (2004) R827355C003 (Final) R827355C009 (Final) |
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Larson TV, Gould T, Simpson CD, Liu L-JS, Claiborn C, Lewtas J. Source apportionment of indoor, outdoor, and personal PM2.5 in Seattle, Washington, using positive matrix factorization. Journal of the Air & Waste Management Association 2004;54(9):1175-1187. |
R827355 (2004) R827355 (Final) R827355C003 (Final) R827355C008 (2003) R827355C008 (Final) R827355C010 (Final) R826371C004 (Final) R826788 (2000) R826788 (Final) |
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Liu L-JS, Slaughter JC, Larson TV. Comparison of light scattering devices and impactors for particulate measurements in indoor, outdoor, and personal environments. Environmental Science & Technology 2002;36(13):2977-2986. |
R827355 (2004) R827355 (Final) R827355C003 (2001) R827355C003 (2002) R827355C003 (Final) R827355C008 (Final) |
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Liu L-JS, Box M, Kalman D, Kaufman J, Koenig J, Larson T, Lumley T, Sheppard L, Wallace L. Exposure assessment of particulate matter for susceptible populations in Seattle. Environmental Health Perspectives 2003;111(7):909-918. |
R827355 (2004) R827355 (Final) R827355C003 (Final) R827355C009 (2002) |
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Needham LL, Ozkaynak H, Whyatt RM, Barr DB, Wang RY, Naeher L, Akland G, Bahadori T, Bradman A, Fortmann R, Liu L-JS, Morandi M, O’Rourke MK, Thomas K, Quackenboss J, Ryan PB, Zartarian V. Exposure assessment in the National Children’s Study: introduction. Environmental Health Perspectives 2005;113(8):1076-1082. |
R827355 (Final) R827355C003 (Final) R831710 (2005) R832141 (2006) |
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Pang Y, Gundel LA, Larson T, Finn D, Liu L-JS, Claiborn CS. Development and evaluation of a personal particulate organic and mass sampler. Environmental Science & Technology 2002;36(23):5205-5210. |
R827355 (2004) R827355 (Final) R827355C003 (2002) R827355C003 (Final) |
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Pang Y, Eatough NL, Modey WK, Eatough DJ. Evaluation of the RAMS continuous monitor for determination of PM2.5 mass including semi-volatile material in Philadelphia, PA. Journal of the Air & Waste Management Association 2002;52(5):563-572. |
R827355 (2001) R827355 (Final) R827355C003 (Final) |
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Quintana PJE, Valenzia JR, Delfino RJ, Liu L-JS. Monitoring of 1-min personal particulate matter exposures in relation to voice-recorded time-activity data. Environmental Research 2001;87(3):199-213. |
R827355 (2004) R827355 (Final) R827355C003 (2001) R827355C003 (Final) |
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Simpson CD, Paulsen M, Dills RL, Liu L-JS, Kalman DA. Determination of methoxyphenols in ambient atmospheric particulate matter: tracers for wood combustion. Environmental Science & Technology 2005;39(2):631-637. |
R827355 (2004) R827355 (Final) R827355C003 (2004) R827355C003 (Final) R827355C010 (2003) R827355C010 (Final) R829584 (2004) |
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Wallace LA, Mitchell H, O’Connor GT, Neas L, Lippmann M, Kattan M, Koenig J,Stout JW, Vaughn BJ, Wallace D, Walter M, Adams K, Liu L-JS. Particle concentrations in inner-city homes of children with asthma: the effect of smoking, cooking and outdoor pollution. Environmental Health Perspectives 2003;111(9):1265-1272. |
R827355 (Final) R827355C002 (2002) R827355C003 (Final) |
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Wu C-F, Delfino RJ, Floro JN, Samimi BS, Quintana PJE, Kleinman MT, Liu L-JS. Evaluation and quality control of personal nephelometers in indoor, outdoor, and personal environments. Journal of Exposure Science and Environmental Epidemiology 2005;15(1):99-110. |
R827355 (Final) R827355C003 (2003) R827355C003 (Final) |
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Wu C-F, Delfino RJ, Floro JN, Quintana PJ, Samimi BS, Kleinman MT, Allen RW, Liu L-JS. Exposure assessment and modeling of particulate matter for asthmatic children using personal nephelometers. Atmospheric Environment 2005;39(19):3457-3469. |
R827355 (Final) R827355C003 (2004) R827355C003 (Final) |
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Wu C-F, Jimenez J, Claiborn C, Gould T, Simpson CD, Larson T, Liu L-JS. Agricultural burning smoke in eastern Washington: Part II. Exposure assessment. Atmospheric Environment 2006;40(28):5379-5392. |
R827355 (Final) R827355C003 (Final) R827355C010 (Final) |
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Wu C-F, Larson TV, Wu S-Y, Williamson J, Westberg HH, Liu L-JS. Source apportionment of PM2.5 and selected hazardous air pollutants in Seattle. The Science of the Total Environment 2007;386(1-3):42-52. |
R827355 (Final) R827355C003 (Final) |
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, Air, Geographic Area, Scientific Discipline, Health, RFA, Susceptibility/Sensitive Population/Genetic Susceptibility, indoor air, Risk Assessments, genetic susceptability, Northwest, Health Risk Assessment, Epidemiology, air toxics, Atmospheric Sciences, Biochemistry, particulate matter, Environmental Chemistry, State, aerosols, exposure assessment, incineration, California (CA), PAHs, exposure and effects, ambient air quality, cardiovascular disease, health effects, hydrocarbons, indoor air quality, inhalation, mortality, allergens, air quality, ambient air, cardiopulmonary response, fine particles, hazardous air pollutants, atmospheric aerosols, cardiopulmonary responses, human health risk, particle exposure, mortality studies, air pollutants, biostatistics, human health effects, particulates, PM 2.5, sensitive populations, toxicology, ambient particle health effects, air pollution, atmospheric chemistry, children, PM10, stratospheric ozone, Seattle, Washington, exposure, human susceptibility, ambient aerosol, asthma, health risks, human exposure, Human Health Risk Assessment, morbidity, animal model, particle transport
Progress and Final Reports:
1999 Progress Report
2000 Progress Report
2001 Progress Report
2002 Progress Report
2003 Progress Report
2004 Progress Report
Original Abstract
Main Center Abstract and Reports:
R827355 Airborne PM - Northwest Research Center for Particulate Air Pollution and Health
Subprojects under this Center:
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R827355C001 Epidemiologic Study of Particulate Matter and Cardiopulmonary
Mortality
R827355C002 Health Effects
R827355C003 Personal PM Exposure Assessment
R827355C004 Characterization of Fine Particulate Matter
R827355C005 Mechanisms of Toxicity of Particulate Matter Using Transgenic Mouse Strains
R827355C006 Toxicology Project -- Controlled Exposure Facility
R827355C007 Health Effects Research Core
R827355C008 Exposure Core
R827355C009 Statistics and Data Core
R827355C010 Biomarker Core
R827355C011 Oxidation Stress Makers