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Influence of human activity patterns, particle composition, and residential air exchange rates on modeled distributions of PM2.5 exposure compared with central-site monitoring data

Abstract

Central-site monitors do not account for factors such as outdoor-to-indoor transport and human activity patterns that influence personal exposures to ambient fine-particulate matter (PM2.5). We describe and compare different ambient PM2.5 exposure estimation approaches that incorporate human activity patterns and time-resolved location-specific particle penetration and persistence indoors. Four approaches were used to estimate exposures to ambient PM2.5 for application to the New Jersey Triggering of Myocardial Infarction Study. These include: Tier 1, central-site PM2.5 mass; Tier 2A, the Stochastic Human Exposure and Dose Simulation (SHEDS) model using literature-based air exchange rates (AERs); Tier 2B, the Lawrence Berkeley National Laboratory (LBNL) Aerosol Penetration and Persistence (APP) and Infiltration models; and Tier 3, the SHEDS model where AERs were estimated using the LBNL Infiltration model. Mean exposure estimates from Tier 2A, 2B, and 3 exposure modeling approaches were lower than Tier 1 central-site PM2.5 mass. Tier 2A estimates differed by season but not across the seven monitoring areas. Tier 2B and 3 geographical patterns appeared to be driven by AERs, while seasonal patterns appeared to be due to variations in PM composition and time activity patterns. These model results demonstrate heterogeneity in exposures that are not captured by the central-site monitor.

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References

  1. Laden F, Schwartz J, Speizer F, Dockery DW . Reduction in fine particulate air pollution and mortality: extended follow-up of the Harvard Six Cities study. Am J Respir Crit Care Med 2006; 173 (6): 667–672.

    Article  CAS  Google Scholar 

  2. Zanobetti A, Schwartz J . The effect of fine and coarse particulate air pollution on mortality: a national analysis. Environ Health Perspect 2009; 117 (6): 898–903.

    Article  Google Scholar 

  3. Pope CA, Ezzati M, Dockery DW . Fine-particulate air pollution and life expectancy in the United States. N Engl J Med 2009; 360 (4): 376–386.

    Article  CAS  Google Scholar 

  4. Appel KW, Bhave PV, Gilliland AB, Sarwar G, Roselle SJ . Evaluation of the community multiscale air quality (CMAQ) model version 4.5: Sensitivities impacting model performance; Part II — particulate matter. Atmos Environ 2008; 42 (24): 6057–6066.

    Article  CAS  Google Scholar 

  5. Lobdell DT, Isakov V, Baxter L, Touma JS, Smuts MB, Özkaynak H . Feasibility of assessing public health impacts of air pollution reduction programs on a local scale: New Haven Case Study. Environ Health Perspect 2011; 119: 487–493.

    Article  Google Scholar 

  6. Beelen R, Hoek G, Fischer P, PAvd Brandt, Brunekreef B . Estimated long-term outdoor air pollution concentrations in a cohort study. Atmos Environ 2007; 41 (7): 1343–1358.

    Article  CAS  Google Scholar 

  7. Aguilera I, Guxens M, Garcia-Esteban R, Corbella T, Nieuwenhuijsen MJ, Foradada CM et al. Association between GIS-based exposure to urban air pollutions during pregnancy and birth wieght in the INMA Sabadell cohort. Environ Health Perspect 2009; 117 (8): 1322–1327.

    Article  CAS  Google Scholar 

  8. Nuckols JR, Ward MH, Jarup L . Using geographic information systems for exposure assessment in environmental epidemiology studies. Environ Health Perspect 2004; 112 (9): 1009–1015.

    Article  Google Scholar 

  9. Burke JM, Zufall MJ, Ozkaynak H . A population exposure model for particulate matter: case study results for PM2.5 in Philadelphia, PA. J Expo Anal Environ Epidemiol 2001; 11: 470–489.

    Article  CAS  Google Scholar 

  10. Blangiardo M, Hansell A, Richardson SA . Bayesian model of time activity data to investigate health effect of air pollution in time series studies. Atmos Environ 2011; 45 (2): 379–386.

    Article  CAS  Google Scholar 

  11. Strand M, Hopke PK, Zhao W, Vedal S, Gelfand E, Rabinovitch N . A study of health effect estimates using competing methods to model personal exposures to ambient PM2.5. J Expo Sci Environ Epidemiol 2007; 17 (6): 549–558.

    Article  CAS  Google Scholar 

  12. Mölter A, Lindley S, de Vocht F, Simpson A, Agius R . Modelling air pollution for epidemiologic research — Part I: A novel approach combining land use regression and air dispersion. Sci Total Environ 2010; 408 (23): 5862–5869.

    Article  Google Scholar 

  13. Jerrett M, Arain A, Kanaroglou P, Beckerman B, Potoglou D, Sahsuvaroglu T et al. A review and evaluation of intraurban air pollution exposure models. J Expo Anal Environ Epidemiol 2005; 15 (2): 185–204.

    Article  CAS  Google Scholar 

  14. Tonne C, Beevers S, Kelly FJ, Jarup L, Wilkinson P, Armstrong B . An approach for estimating the health effects of changes over time in air pollution: an illustration using cardio-respiratory hospital admissions in London. Occup Environ Med 2010; 67: 422–427.

    Article  CAS  Google Scholar 

  15. Özkaynak H, Palma T, Touma JS, Thurman J . Modeling population exposures to outdoor sources of hazardous air pollutants. J Expo Sci Environ Epidemiol 2007; 18 (1): 45–58.

    Article  Google Scholar 

  16. Georgopoulos PG, Wang S-W, Vyas VM, Qing S, Burke J, Vedantham R et al. A source-to-dose assessment of population exposures to fine PM and ozone in Philadelphia, PA, during a summer 1999 episode. J Exp Anal Environ Epidemiol 2005; 15 (5): 439–457.

    Article  CAS  Google Scholar 

  17. Isakov V, Touma JS, Burke J, Lobdell DT, Palma T, Rosenbaum A et al. Combining regional- and local-scale air quality models with exposure models for use in environmental health studies. J Air Waste Manage Assoc 2009; 59 (4): 461–472.

    Article  CAS  Google Scholar 

  18. Strand M, Vedal S, Rodes C, Dutton SJ, Gelfand EW, Rabinovitch N . Estimating effects of ambient PM2.5 exposure on health using PM2.5 component measurements and regression calibration. J Expo Sci Environ Epidemiol 2005; 16 (1): 30–38.

    Article  Google Scholar 

  19. Rich DQ, Kipen HM, Zhang J, Kamat L, Wilson AC, Kostis JB et al. Triggering of transmural infarctions, but not nontransmural infarctions, by ambient fine particles. Environ Health Perspect 2010; 118: 9.

    Article  Google Scholar 

  20. Cao Y, Frey HC . Geographic differences in inter-individual variability of human exposure to fine particulate matter. Atmos Environ 2011; 45 (32): 5684–5691.

    Article  CAS  Google Scholar 

  21. Hering SV, Lunden MM, Thatcher TL, Kirchstetter TW, Brown NJ . Using regional data and building leakage to assess indoor concentrations of particles of outdoor origin. Aerosol Sci Technol 2007; 41 (7): 639–654.

    Article  Google Scholar 

  22. Lunden MM, Thatcher TL, Hering SV, Brown NJ . Use of time- and chemically resolved particulate data to characterize the infiltration of outdoor PM2.5 into a residence in the San Joaquin Valley. Environ Sci Technol 2003; 37 (20): 4724–4732.

    Article  CAS  Google Scholar 

  23. McCurdy T, Glen G, Smith L, Lakkadi Y . The National Exposure Research Laboratory’s Consolidated Human Activity Database. J Expo Anal Environ Epidemiol 2000; 10 (5): 1–13.

    Google Scholar 

  24. Hodas N, Lunden MM, Meng QY, Baxter LK, Özkaynak H, Burke J et al. Heterogeneity in the fraction of ambient PM2.5 found indoors contributes exposure error and may contribute to spatial and temporal differences in reported PM2 .5 health effect estimates. J Expo Sci Environ Epidemiol 2012; 22 (5): 448–454.

    Article  CAS  Google Scholar 

  25. Chan WR, Nazaroff WW, Price PN, Sohn MD, Gadgil AJ . Analyzing a database of residential air leakage in the United States. Atmos Environ 2005; 39: 3444–3455.

    Google Scholar 

  26. Sherman M, Dickerhoff D . Air-tightness of US Dwellings. Lawrence Berkeley Laboratory: Buxton, UK. 1994 contract no.: LBL-35700.

    Google Scholar 

  27. United States Census Bureau. American Housing Survey for the Philadelphia Metropolitan Area. United States Census Bureau. 2004.

  28. United States Census Bureau. American Housing Survey for the Northern New Jersey Metropolitan Area. United States Census Bureau. 2004 contract no.: Series H170/03-10.

  29. SAS Institute Inc. Version 9.3 of the SAS System for Windows. SAS Institute Inc.: Cary, NC. 2011 Copyright 2002–2010.

  30. Lunden MM, Revzan KL, Fischer ML, Thatcher TL, Littlejohn D, Hering SV et al. The transformation of outdoor ammonium nitrate aerosols in the indoor environment. Atmos Environ 2003; 37 (39-40): 5633–5644.

    Article  CAS  Google Scholar 

  31. Murray DM, Burmaster DE . Residential air exchange rates in the United States: empirical and estimated parametric distributions by season and climatic region. Risk Anal 1995; 15 (4): 459–465.

    Article  Google Scholar 

  32. Setton E, Marshall JD, Brauer M, Lundquist KR, Hystad P, Keller P et al. The impact of daily mobility on exposure to traffic-related air pollution and health effect estimates. J Expo Sci Environ Epidemiol 2011; 21 (1): 42–48.

    Article  Google Scholar 

Download references

Acknowledgements

This research was funded in part by the U.S. Environmental Protection Agency (Cooperative Agreement CR-83407201-0), NIEHS-sponsored UMDNJ Center for Environmental Exposures and Disease (NIEHS P30ES005022), and the New Jersey Agricultural Experiment Station. Natasha Hodas was supported by a Graduate Assistance in Areas of National Need Fellowship and an EPA STAR Fellowship. Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy. We thank Kristin Isaacs of the US EPA’s National Exposure Laboratory and Tom Long of the US EPA’s National Center for Environmental Assessment for their scientific guidance on this manuscript.

Disclaimer

The US Environmental Protection Agency through its Office of Research and Development funded and collaborated the research described here under Cooperative Agreement CR-83407201-0 to Rutgers University. It has been subjected to Agency review and approved for publication.

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Correspondence to Lisa K Baxter.

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Supplementary Information accompanies the paper on the Journal of Exposure Science and Environmental Epidemiology website

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Baxter, L., Burke, J., Lunden, M. et al. Influence of human activity patterns, particle composition, and residential air exchange rates on modeled distributions of PM2.5 exposure compared with central-site monitoring data. J Expo Sci Environ Epidemiol 23, 241–247 (2013). https://doi.org/10.1038/jes.2012.118

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