Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Research Article
  • Published:

Estimation of ambient and non-ambient components of particulate matter exposure from a personal monitoring panel study

Journal of Exposure Science & Environmental Epidemiology volume 16, pages 264–274 (2006)Cite this article

Abstract

To provide additional insight into factors affecting exposure to airborne particulate matter and the resultant health effects, we developed a method to estimate the ambient and nonambient components of total personal exposure. The ambient (or outdoor) component of total personal exposure to particulate matter (PM) (called ambient exposure) includes exposure to the ambient PM concentration while outdoors and exposure while indoors to ambient PM that has infiltrated indoors. The nonambient component of total personal exposure to PM (called nonambient exposure) refers to exposure to PM generated by indoor sources and an individual's personal activity. We used data collected from a personal monitoring study in Vancouver, Canada to demonstrate the methodology. In this study, ambient PM2.5 exposure was 71% of the measured ambient PM2.5 concentration and was responsible for 44% of the measured total personal PM2.5 exposure. Regression analysis of the pooled data sets for ambient and total exposure against outdoor concentrations yielded similar slopes (0.76 for ambient and 0.77 for total) but a higher coefficient of determination for ambient exposure (R2=0.62) than for total exposure (R2=0.072). As expected, the nonambient exposure was not related to the ambient concentration (R2<10−6). For longitudinal analyses of the relationship between measured personal exposure and ambient concentrations for individual subjects, the correlation of total personal exposure with ambient concentration yielded values of Pearson's r from 0.83 to −0.68 with an average of 0.36. The relationship was statistically significant for only five of the 16 subjects. In contrast, the correlation of the estimated ambient exposure with ambient concentration yielded values of Pearson's r from 0.92 to 0.77 with an average of 0.88; 14 were significant. An example, taken from an epidemiologic analysis using the exposure data from this paper, demonstrates the usefulness of separating total exposure into its ambient and nonambient components.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 4
Figure 5
Figure 2
Figure 3
Figure 6
Figure 7
Figure 8

Similar content being viewed by others

References

  • Babich P., et al. Method comparisons for particulate nitrate, elemental carbon, and PM2.5 mass in seven US cities. J Air Waste Manage Assoc 2000: 50: 1095–1105.

    Article  CAS  Google Scholar 

  • Brauer M., et al. Exposure of chronic obstructive pulmonary disease patients to particles: respiratory and cardiovascular health effects. J Exposure Anal Environ Epidemiol 2001: 11: 490–500.

    Article  CAS  Google Scholar 

  • Dockery D.W., and Spengler J.D. Indoor–outdoor relationships of respirable sulfates and particles. Atmos Environ 1981: 15: 335–343.

    Article  CAS  Google Scholar 

  • Duan N. Models for human exposure to air pollution. Environ Int 1982: 8: 305–309.

    Article  CAS  Google Scholar 

  • Ebelt S.T., et al. Exposure of chronic obstructive pulmonary disease patients to particulate matter: relationships between personal and ambient air concentrations. J Air Waste Manage Assoc 2000: 50: 1081–1094.

    Article  CAS  Google Scholar 

  • Ebelt S.T., et al. Exposure to ambient and nonambient components of particulate matter: a comparison of health effects. Epidemiology 2005: 16: 396–405.

    Article  Google Scholar 

  • Ebelt S.T. Particulate air pollution and chronic obstructive pulmonary disease patients: an assessment of exposure and cardiovascular health effects, (MSc thesis), University of British Columbia: Vancouver, British Columbia, Canada 2000.

  • Liu L.J., et al. Exposure assessment of particulate matter for susceptible populations in Seattle. Environ Health Perspect 2003: 11: 909–918.

    Article  Google Scholar 

  • Long C.M., et al. Characterization of indoor particle sources using continuous mass and size monitors. J Air Waste Manage Assoc 2000: 50: 1236–1250.

    Article  CAS  Google Scholar 

  • Long C.M., et al. A pilot investigation of the relative toxicity of indoor and outdoor fine particles: in vitro effects of endotoxin and other particulate properties. Environ Health Perspect 2001a: 109: 1019–1026.

    Article  CAS  Google Scholar 

  • Long C.M., et al. Using time- and size-resolved particulate data to quantify indoor penetration and deposition behavior. Environ Sci Technol 2001b: 35: 2089–2099.

    Article  CAS  Google Scholar 

  • Mage D., et al. Assessment of human exposure to ambient particulate matter. J Air Waste Manage Assoc 1999: 49: 174–185.

    Article  Google Scholar 

  • Marple V.A., et al. Low flow rate sharp cut impactors for indoor air sampling: design and calibration. JAPCA 1987: 37: 1303–1307.

    Article  CAS  Google Scholar 

  • National Research Council. Research Priorities for Airborne Particulate Matter. I. Immediate Priorities and a Long-Range Research Portfolio. National Academy Press, Washington, DC, 1998 Available: http://www.nap.edu/catalog/6131.html[4 June 2003].

  • Ollison W. Indoor Sources of Sulfate. National Academies, Division on Earth and Life Studies 2004. Available: http://dels.nas.edu/best/pm/presentations/exp/ollison_overview.pdf[23 November, 2004].

  • Ott W., et al. Predicting particulate (PM10) personal exposure distributions using a random component superposition statistical model. J Air Waste Manage Assoc 2000: 50: 1390–1406.

    Article  CAS  Google Scholar 

  • Özkaynak H., et al. The Particle TEAM (PTEAM) Study: Analysis of the Data: Final Report, Vol. III. U.S. Environmental Protection Agency, Atmospheric Research and Exposure Assessment Laboratory, Research Triangle Park, NC, 1996a, report no. EPA/600/R-95/098. Available from NTIS, Springfield, VA; PB97-102495.

    Google Scholar 

  • Özkaynak H., et al. Personal exposures to airborne particles and metals: results from the particle TEAM study in Riverside, California. Exposure Anal Environ Epidemiol 1996b: 6: 57–78.

    Google Scholar 

  • Sarnat J.A., et al. Using sulfur as a tracer of outdoor fine particulate matter. Environ Sci Technol 2002: 36: 5305–5314.

    Article  CAS  Google Scholar 

  • Thomas K.W., et al. Particle total exposure assessment methodology (PTEAM) 1990 study: method performance and data quality for personal, indoor, and outdoor monitoring. J Exposure Anal Environ Epidemiol 1993: 3: 203–226.

    CAS  Google Scholar 

  • U.S. Code. Clean Air Act, §109, National Ambient Air Quality Standards, U.S.C. 42 1998; §7409.

  • U.S. Environmental Protection Agency. Particulate matter research needs for human health risk assessment to support future reviews of the National Ambient Air Quality Standards for particulate matter. National Center for Environmental Assessment, Research Triangle Park, NC, 1998, report no. EPA/600/R-97/132F.

  • Weisel C.P., et al. Relationship of indoor, outdoor and personal air (RIOPA) study: study design, methods and quality assurance/control results. J Exposure Anal Environ Epidemiol 2005: 15: 123–137.

    Article  CAS  Google Scholar 

  • Whitby K.T. The physical characteristics of sulfur aerosols. Atmos Environ 1978: 12: 135–159.

    Article  CAS  Google Scholar 

  • Williams R., et al. Indoor, outdoor, and personal exposure monitoring of particulate air pollution: the Baltimore elderly epidemiology-exposure pilot study. Atmos Environ 2000a: 34: 4193–4204.

    Article  CAS  Google Scholar 

  • Williams R., et al. Comparison of PM2.5 and PM10 monitors. J Exposure Anal Environ Epidemiol 2000b: 10: 497–505.

    Article  CAS  Google Scholar 

  • Williams R., et al. The 1998 Baltimore Particulate Matter Epidemiology-Exposure Study: Part 1. Comparison of ambient, residential outdoor, indoor and apartment particulate matter monitoring. J Exposure Anal Environ Epidemiol 2000c: 10: 518–532.

    Article  CAS  Google Scholar 

  • Williams R., et al. The Research Triangle Park particulate matter panel study: modeling ambient source contribution to personal and residential PM mass concentrations. Atmos Environ 2003a: 37: 5365–5378.

    Article  CAS  Google Scholar 

  • Williams R., et al. The Research Triangle Park particulate matter panel study: PM mass concentration relationships. Atmos Environ 2003b: 37: 5349–5363.

    Article  CAS  Google Scholar 

  • Wilson W.E., et al. Estimating separately personal exposure to ambient and nonambient particulate matter for epidemiology and risk assessment: why and how. J Air Waste Manage Assoc 2000: 50: 1167–1183.

    Article  CAS  Google Scholar 

  • Wilson W.E., and Brauer M. Methods to estimate the ambient and nonambient components of total personal exposure to particulate matter. Presented at the 11th Annual Conference of the International Society of Exposure Assessment, November 2001, Charleston, SC.

  • Wilson W.E., and Suh H.H. Differentiating fine and coarse particles: definitions and exposure relationships relevant to epidemiological studies. In: Schmidt-Ott A. (Ed.). Trends in Aerosol Research IV: New Approaches in Aerosol Science and Technology, Proceedings of the Seminar. Gerhard Mercator University, Duisburg, Germany; Gerhard Mercator University of Duisburg, Duisburg, Germany; January 1995 pp. 57–71.

    Google Scholar 

  • Wilson W.E., and Suh H.H. Fine particles and coarse particles: concentration relationships relevant to epidemiologic studies. J Air Waste Manage Assoc 1997: 47: 1238–1249.

    Article  CAS  Google Scholar 

  • Zeger S.L., et al. Exposure measurement error in time-series studies of air pollution: concepts and consequences. Environ Health Perspect 2000: 108: 419–426.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The collection of the data that are analyzed in this manuscript was supported in part by a research grant from the British Columbia Lung Association and by an Infrastructure Program (Center for Health and Environment Research) award from the Michael Smith Foundation for Health Research. Portions of this work were first reported at the Annual Conference of the International Society of Exposure Assessment, Charleston, SC, November 4–8, 2001 (Wilson and Brauer, 2001).

Author information

Authors and Affiliations

  1. National Center for Environmental Assessment, U.S. Environmental Protection Agency, Research Triangle Park, 27711, North Carolina, USA

    William E Wilson

  2. School of Occupational and Environmental Hygiene, The University of British Columbia, Vancouver, V6T1Z3, British Columbia, Canada

    Michael Brauer

Authors
  1. William E Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Brauer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to William E Wilson.

Additional information

Disclaimer

This paper has been reviewed in accordance with U.S. Environmental Protection Agency policy and approved for publication. However, the views expressed are those of the authors and do not necessarily reflect the views or policies of the Environmental Protection Agency. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.

Appendices

Appendix A1. Summary of notation, symbols, definitions, and equations

Exposure and Concentration Variables

T :

total personal exposure as measured by a monitor worn by an individual

N :

nonambient exposure, that is, the component of total exposure due to indoor sources, while indoors, plus “personal cloud” or personal-activity sources, while indoors or outdoors

A :

ambient exposure, that is, exposure to ambient particles, while outdoors, plus exposure, while indoors, to particles that have infiltrated from outdoors to indoors

C :

ambient concentration, as measured at a community monitoring site or the average of several community monitoring sites

C ai :

concentration of ambient PM infiltrated indoors from outdoors

C in :

concentration of PM in indoor air

F INF :

Cai/C=pa/(a+k)=infiltration factor

α :

A/C=y+(1-y) {pa/(a+k)}=attenuation factor

Mass Balance Equation and Derived Equations

Parameters Used in the Mass Balance Equation

Superscripts used to identify size fractions or chemical components

Thus, C2.5 refers to the ambient concentration of PM2.5 and AijSO4 2.5 would refer to the ambient exposure to sulfate in PM2.5. As in this study sulfate was only measured in PM2.5, we simplify the notation so that A SO 4 refers to ambient exposure to sulfate in PM2.5.

Appendix B1. Quality assurance

Data Collection

Three PM samplers were used in the Vancouver panel study, personal exposure monitors (PEM), Harvard Impactors, and the Tapered Element Oscillating Microbalance (TEOM). Prior to the Vancouver panel study, collocation and intercomparison experiments were made with the PEM and the Harvard Impactors. The results indicated that the PEMs gave readings that were on average 1.5 μg/m3 higher than the Harvard Impactors but that this difference was less than the difference between collocated PEMS (mean difference of 2.3 μg/m3). Therefore, no adjustments were made to the values measured in the study. Other studies have also found higher values for the PEMs, especially for intercomparisons conducted indoors (Williams et al., 2000a, 2000b; Weisel et al., 2005). These differences may be related to differences in the loss of ammonium nitrate and semi-volatile organic compounds due to evaporation during sampling. The PEM has a lower face velocity and pressure drop across the filter and, therefore, would be expected to retain more of the semi-volatile components. The observation from an intercomparison of PM10 monitors (Thomas et al., 1993) that PEMs gave median PM10 concentrations that were approximately 9% higher than the dichotomous sampler and 16% higher than the high-volume sampler are consistent with this explanation. Other studies have examined the agreement among the PEM, the Harvard Impactor, and the Federal Reference Method for PM2.5 and measured the precision of the PEM and the Harvard Impactor (Babich et al., 2000; Liu et al, 2003; Williams et al, 2000b, 2000c). There do not appear to be any intercomparisons of the TEOM with PEMs, but the TEOM is classified as an equivalent method for PM10. In general, while exposure analysts have been concerned with the differences among the various PM monitors, they have concluded that the measurements are adequate to conduct reliable exposure studies.

Data Analysis

The Vancouver study had monitoring data from five sites in the neighborhood in which the subjects lived. It was necessary to decide whether to use the ambient concentration data from the monitoring site closest to each subject's home or to use the value obtained by averaging the concentrations data from all five sites. The closest site would not be the most representative site if the site, but not the subject's home, were impacted by a local source. Averaging across five sites would also reduce the effect of a measurement error at the closest site. Therefore, both possibilities were examined.

The initial simple least squares regression analysis of T2.5 vs. C2.5 for the entire data set yields an ā of 0.32 for the closest sites and an ā of 0.52 for the average of five sites. Both values of ā seem unreasonably low for PM2.5 for the summer–fall period for subjects who mostly kept their windows open. These values were also low compared to the ā obtained from the regression of T SO 4 against C SO 4 . Two additional regression models, which gave less weight to points farthest from the regression line, were tried. They gave increased values for ā, but lower and unrealistic values for N. Therefore, some quality control checks on the data (Cj and Tij) were performed. As a point with a high T and a low C, or a point with a low T and a high C, would unduly reduce the value of α obtained from the regression, the distribution of Tij/Cj was examined. Two high values were found that were well outside the distribution of the remaining points. After removing these outliers, the simple least squares regression of Tij against the average Cij yielded ā=0.77, in good agreement with ā=0.74 from the regression of T SO 4 against C SO 4 and the ā=0.75 obtained from the average of the individual sulfate α i j SO 4 = T i j SO 4 / C i SO 4 . However, even with the outliers deleted, the slope obtained from the regression with the closest site was too low (ā=0.56). Therefore, it was decided to use the average of the five monitoring sites in the further analysis of the data. Table B1 shows the results of the four regression analyses. If the interest is in obtaining a meaningful ā and N from a regression analysis, it will be necessary to check for and, if necessary, remove points that are clearly outside the distribution. However, if the interest is in determining if C is an adequate surrogate for T, the outliers should be kept in the analysis unless it can be shown that they are owing to error rather than unusually large nonambient exposures.

Table a1 Results of regression analyses using ambient concentration values either from the closest site to each subject's home or the average of the five monitoring sites
Full size table

The average of the ambient concentrations from the five sites was used for the statistical analyses and discussion in this paper. However, in estimating values for use in the epidemiologic analysis (Ebelt et al., 2005), we performed some additional adjustments in order to obtain the most appropriate values for each individual subject. For two subjects, T SO 4 / C SO 4 values were mostly greater than 1, and the regression of T SO 4 vs. C SO 4 gave a positive intercept, suggesting an indoor source of sulfate. We could not identify, however, any sources of indoor sulfate based on time-activity data. In future studies, it would be useful to check for possible indoor sources such as use of ultrasonic humidifiers, matches, kerosene heaters, inhalers using medications in the form of sulfates, or placing the personal sampler where it would be exposed to particles from evaporation of droplets from showering. For the two subjects with a positive intercept, the average α SO 4 , as given by the slope of T SO 4 vs. C SO 4 for each individual, was used. In both cases, the regression of T SO 4 on C SO 4 yielded a high correlation and, therefore, a reliable value of . The values of were then multiplied by the corresponding daily C2.5 value to obtain Aij2.5 (Eq. (A.5)), followed by estimation of Nij2.5 using Eq. (A.4). An additional 11 values of α i j SO 4 were >1. For these cases, the α i j SO 4 < 1 was replaced by the average of the other daily values of α i j SO 4 for the subject.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wilson, W., Brauer, M. Estimation of ambient and non-ambient components of particulate matter exposure from a personal monitoring panel study. J Expo Sci Environ Epidemiol 16, 264–274 (2006). https://doi.org/10.1038/sj.jes.7500483

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/sj.jes.7500483

Keywords

This article is cited by

Search

Advanced search

Quick links