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Final Report: Exposure Characterization Error
EPA Grant Number: R827351C001Subproject: this is subproject number 001 , established and managed by the Center Director under grant R827351
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
Center: EPA NYU PM Center: Health Risks of PM Components
Center Director: Lippmann, Morton
Title: Exposure Characterization Error
Investigators: Ito, Kazuhiko
Institution: NYU School of Medicine
EPA Project Officer: Stacey Katz/Gail Robarge,
Project Period: June 1, 1999 through May 31, 2005 (Extended to May 31, 2006)
RFA: Airborne Particulate Matter (PM) Centers (1999)
Research Category: Particulate Matter
Description:
Objective:The main objective of this project was to quantitatively characterize spatio-temporal error of particulate matter (PM) and gaseous co-pollutants measured at routine regulatory-based air monitors as a function of site characteristics. The rationale was that the closer association of PM10 with health outcomes than those of criteria air pollutants may have been due, in part, to differential exposure characterization error, i.e., PM10 may have less error. Furthermore, we expected that PM10 might have varying exposure characterization error across U.S. due to varying source types, which may result in heterogeneity of estimated PM10 risk estimates across cities. The prevailing hypothesis was that the PM10 and gaseous co-pollutants data from a single air monitoring station could adequately reflect the population exposure for the entire city. Also, during the course of this project, the new PM2.5 chemical speciation network’s data became available (from ~ 2001), and we examined exposure characterization error across components of PM2.5.
Summary/Accomplishments (Outputs/Outcomes):In the analyses of monitor-to-monitor correlation for PM10 and gaseous criteria pollutants in 7 North-Central States for the years 1988-1990 (Ito, et al., 2001), O3, PM10, and NO2 had generally higher monitor-to-monitor temporal correlations (r: 0.8-0.6) than CO or SO2 (r<0.5). For the nationwide data (Ito, et al., 2005), the overall average rankings in monitor-to-monitor correlations were, in descending order: O3, NO2, and PM10, (r ~ 0.6 to 0.8) > CO (r < 0.6) > SO2 (r < 0.5). The correlations were modeled as a function of qualitative site characteristics (i.e., land-use, location-setting, and monitoring-objective), and quantitative information (median separation distance, longitude/latitude or regional indicators) for each pollutant using Generalized Additive Models (GAM). Both separation distance and regional variation were important predictors of the correlation. For PM10, the correlation for the monitors along the East Coast was higher by ~0.2 than for western regions. The qualitative monitor characteristics were often significant predictors of the variation in correlation, but their impacts were not substantial in magnitude for most categories. Thus, the apparent regional heterogeneity in PM effect estimates, as well as the differences in the significance of health outcome associations across pollutants may, in part, be explained by the differences in monitor-to-monitor correlations. To examine this issue, we conducted a regression analysis to see if the heterogeneity of PM10 risk estimates across the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) 90 cities were due to PM10 monitor-to-monitor correlation. For the 83 cities that could be matched, the inverse-variance weighted regression of PM10 mortality risk estimates on the median PM10 correlation showed a positive prediction of PM10 mortality risk estimates with a slope of 0.14 (95%CI: [-0.02, 0.30]) per 0.1 increment of PM10 correlation.
Possible exposure characterization error across PM2.5 components were examined using PM2.5 chemical speciation data for 3 locations in New York City (Ito, et al., 2004). Secondary aerosol species (e.g., SO4, NH4, NO3, organic carbon [OC], etc.) tended to show high monitor-to-monitor correlations, whereas the species associated with more local sources (e.g., elemental carbon [EC] as a traffic source marker) showed lower correlations. Source-apportionment was also conducted for each monitor’s data. The estimated source-apportioned PM2.5 mass generally showed the highest monitor-to-monitor correlation for the secondary aerosol factor (r range: 0.72–0.93). The correlation for the more localized traffic-related factor was more variable (r range: 0.26–0.95). The estimated mean PM2.5 mass contributions by source/pollution type across the monitors varied least for the secondary aerosol factor. We extended the analysis to 28 metropolitan statistical areas (MSAs) where multiple monitors generated PM2.5 chemical speciation data for the years 2001-2003. We analyzed a set of key PM2.5 components that were of interest in terms of toxicological effects, source signature, and generally large signal-to-noise ratios: i.e., Ni, V, Pb, Cr, Mn, Fe, Si, As, Se, SO4, NH4, NO3, EC, and OC. Again, the species associated with secondary aerosols (e.g., SO4, NO3) showed high monitor-to-monitor correlation. However, the monitor-to-monitor correlation for other species varied widely across the MSAs, likely reflecting the variation in the levels and major source types across the MSAs.
The monitor-to-monitor correlations are pertinent to the interpretation of results from short-term effects (i.e., time-series and longitudinal) studies. We also examined potential exposure characterization errors pertinent to long-term effects (i.e., cohort and cross-sectional) studies, using the same 28 MSAs. We found that, for the key PM2.5 components, the coefficient of variation (CV) for across-MSA variation was generally far larger than those for within-MSA variations, with only a few exceptions. The result suggests that the quality of spatial resolution of the key PM2.5 components is sufficient and adequate for the analysis of cross-sectional cohort data.
The results from the Source Apportionment Workshop (Thurston, et al., 2005; Hopke, et al., 2006; Ito, et al., 2006; Mar, et al., 2006) also provided information regarding exposure characterization errors for source-apportioned PM2.5. Comparisons of source-apportioned PM2.5 across investigators for PM2.5 chemical speciation data sets from Phoenix, AZ and Washington, DC found that soil-, secondary sulfate-, residual oil combustion-, and salt-associated mass were most unambiguously identified by various methods, whereas vegetative burning and traffic were less consistently identified. Combined with the result suggestive of varying exposure characterization error across PM2.5 species, and U.S. regions, a systematic examination of multi-city time-series health effects analysis will be needed. We compared the mean levels of key PM2.5 chemical species and the published PM10 mortality risk estimates in 60 MSA’s in the NMMAPS study, and found that the city-to-city variation of PM10 risk estimates could be better explained by some PM2.5 chemical species (Ni and V) than others (Lippmann, et al., 2006). Thus, the city-to-city variation in PM health risk estimates may be modified by components of PM2.5.
Conclusions:There are differential exposure characterization errors across PM and gaseous pollutants at ambient levels. PM10, PM2.5, O3, and NO2 have moderate to high temporal correlations within cities, compared to CO and SO2. These errors also vary by region and site-specific characteristics. Some of the differences in the observed health effects across pollutants in past health effect studies may be explained by our findings. However, the estimated ecologic level exposure characterization errors did not explain the city-to-city variation in the PM10 mortality risk estimates substantively. Components of PM may play roles in the city-to-city variation of PM health risks. A more comprehensive assessment of the overall exposure characterization error will need to consider personal level exposure error.
References:
Hopke PK, Ito K, Mar T, Christensen WF, Eatough DJ, Henry RC, Kim E, Laden F, Lall R, Larson TV, Liu H, Neas L, Pinto J, Stolzel M, Suh H, Paatero P, Thurston GD. PM source apportionment and health effects. 1. Intercomparison of source apportionment results. Journal of Exposure Analysis and Environmental Epidemiology 2006;16:275-286.
Ito K, Christensen WF, Eatough DJ, Henry RC, Kim E, Laden F, Lall R, Larson TV, Neas L, Hopke PK, Thurston GD. PM source apportionment and health effects. 2. An investigation of inter-method variability in associations between source-apportioned fine particle mass and daily mortality in Washington, DC. Journal of Exposure Analysis and Environmental Epidemiology 2006;16:300-310.
Ito K, Thurston GD, Nadas A, Lippmann M. Monitor-to-monitor temporal correlation of air pollution and weather variables in the North-Central U.S. Journal of Exposure Analysis and Environmental Epidemiology 2001;11:21-32.
Ito K, Xue N, Thurston GD. Spatial variation of PM2.5 chemical species and source-apportioned mass concentrations in New York City. Atmospheric Environment 2004;38:5269-5282.
Ito K, DeLeon SF, Nadas A, Thurston GD, Lippmann M. Monitor-to-monitor temporal correlation of air pollution in the contiguous U.S. Journal of Exposure Analysis and Environmental Epidemiology 2005;15:172-184.
Mar TF, Ito K, Koenig JQ, Larson TV, Christensen WF, Eatough DJ, Henry RC, Kim E, Laden F, Lall R, Neas L, Hopke PK, Thurston GD. PM source apportionment and health effects. 3. An investigation of inter-method variability in associations between source apportioned fine particle mass and daily mortality in Phoenix, AZ. Journal of Exposure Analysis and Environmental Epidemiology 2006;16:311-320.
Thurston GD, Ito K, Mar T, Christensen WF, Eatough DJ, Henry RC, Kim E, Laden F, Lall R, Larson TV, Liu H, Neas L, Pinto J, Stolzel M, Suh H, Paatero P, Hopke PK. The Workshop on the Source Apportionment of PM Health Effects: Inter-Comparison of Results and Implications. Environmental Health Perspectives 2005;113:1768-1774.
Technical Report:
Long Version of Final Report (PDF) (5 pp, 48 K, About PDF)
Journal Articles on this Report: 7 Displayed | Download in RIS Format
| Other subproject views: | All 7 publications | 7 publications in selected types | All 7 journal articles |
| Other center views: | All 95 publications | 86 publications in selected types | All 74 journal articles |
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Hopke PK, Ito K, Mar T, Christensen WF, Eatough DJ, Henry RC, Kim E, Laden F, Lall R, Larson TV, Liu H, Neas L, Pinto J, Stolzel M, Suh H, Paatero P, Thurston GD. PM source apportionment and health effects: 1. Intercomparison of source apportionment results. Journal of Exposure Science & Environmental Epidemiology 2006;16(3):275-286. |
R827351 (Final) R827351C001 (Final) |
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Ito K, Thurston GD, Nadas A, Lippmann M. Monitor-to-monitor temporal correlation of air pollution and weather variables in the North-Central U.S. Journal of Exposure Analysis & Environmental Epidemiology 2001;11(1):21-32. |
R827351 (Final) R827351C001 (2000) R827351C001 (2002) R827351C001 (2003) R827351C001 (Final) R825271 (Final) |
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Ito K, Xue N, Thurston G. Spatial variation of PM2.5 chemical species and source-apportioned mass concentrations in New York City. Atmospheric Environment 2004;38(31):5269-5282. |
R827351 (2003) R827351 (Final) R827351C001 (2003) R827351C001 (Final) R827997 (2005) R827997 (Final) |
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Ito K, De Leon SF, Thurston GD, Nadas A, Lippmann M. Monitor-to-monitor temporal correlation of air pollution in the contiguous US. Journal of Exposure Analysis and Environmental Epidemiology 2005;15(2):172-184. |
R827351 (2003) R827351 (Final) R827351C001 (2003) R827351C001 (Final) |
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Ito K, Christiansen WF, Eatough DJ, Henry RC, Kim E, Laden F, Lall R, Larson TV, Neas L, Hopke PK, Thurston GD. PM source apportionment and health effects: 2. An investigation of intermethod variability in associations between source-apportioned fine particle mass and daily mortality in Washington, DC. Journal of Exposure Science & Environmental Epidemiology 2006;16(4):300-310. |
R827351 (Final) R827351C001 (Final) R827353 (Final) R827353C015 (Final) R827354 (Final) R827354C001 (Final) R827355 (Final) R827355C008 (Final) R827997 (Final) |
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Lippmann M, Ito K, Hwang JS, Maciejczyk P, Chen LC. Cardiovascular effects of nickel in ambient air. Environmental Health Perspectives 2006;114(11):1662-1669. |
R827351 (Final) R827351C001 (Final) |
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Thurston GD, Ito K, Mar T, Christensen WF, Eatough DJ, Henry RC, Kim E, Laden F, Lall R, Larson TV, Liu H, Neas L, Pinto J, Stotzel M, Suh H, Hopke PK. Workgroup report: workshop on source apportionment of particulate matter health effects--Inter-Comparison of results and implications. Environmental Health Perspectives 2005;113(12):1768-1774. |
R827351 (Final) R827351C001 (Final) R827353 (Final) R827353C015 (Final) R827354 (Final) R827354C001 (Final) R827355 (Final) R827355C008 (Final) |
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subchronic, source apportionment, oxidative stress, PM component,
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ENVIRONMENTAL MANAGEMENT, Air, Scientific Discipline, Health, RFA, PHYSICAL ASPECTS, Risk Assessment, Risk Assessments, Health Risk Assessment, Physical Processes, Atmosphere, Atmospheric Sciences, particulate matter, Environmental Chemistry, Environmental Monitoring, exposure assessment, ambient air quality, chemical characteristics, ozone, human health risk, air toxics, particulates, Sulfur dioxide, toxicology, atmospheric particles, ozone monitoring, air pollution, environmental risks, ambient air monitoring, atmospheric particulate matter, exposure, atmospheric aerosol particles, airborne particulate matter, human exposure, PM
Relevant Websites:
Long Version of Final Report (PDF) (5 pp, 48 K, About PDF)
http://www.med.nyu.edu/environmental/
http://es.epa.gov/ncer/science/pm/centers.html
Progress and Final Reports:
1999 Progress Report
2000 Progress Report
2001 Progress Report
2002 Progress Report
2003 Progress Report
Original Abstract
Main Center Abstract and Reports:
R827351 EPA NYU PM Center: Health Risks of PM Components
Subprojects under this Center:
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R827351C001 Exposure Characterization Error
R827351C002 X-ray CT-based Assessment of Variations in Human Airway Geometry: Implications for Evaluation of Particle Deposition and Dose to Different Populations
R827351C003 Asthma Susceptibility to PM2.5
R827351C004 Health Effects of Ambient Air PM in Controlled Human Exposures
R827351C005 Physicochemical Parameters of Combustion Generated Atmospheres as Determinants of PM Toxicity
R827351C006 Effects of Particle-Associated Irritants on the Cardiovascular System
R827351C007 Role of PM-Associated Transition Metals in Exacerbating Infectious Pneumoniae in Exposed Rats
R827351C008 Immunomodulation by PM: Role of Metal Composition and Pulmonary Phagocyte Iron Status
R827351C009 Health Risks of Particulate Matter Components: Center Service Core
R827351C010 Lung Hypoxia as Potential Mechanisms for PM-Induced Health Effects
R827351C011 Urban PM2.5 Surface Chemistry and Interactions with Bronchoalveolar Lavage Fluid (BALF)
R827351C012 Subchronic PM2.5 Exposure Study at the NYU PM Center
R827351C013 Long Term Health Effects of Concentrated Ambient PM2.5
R827351C014 PM Components and NYC Respiratory and Cardiovascular Morbidity
R827351C015 Development of a Real-Time Monitoring System for Acidity and Soluble Components in Airborne Particulate Matter
R827351C016 Automated Real-Time Ambient Fine PM Monitoring System