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  • Research Article
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Evaluation of the recursive model approach for estimating particulate matter infiltration efficiencies using continuous light scattering data

Abstract

Quantifying particulate matter (PM) infiltration efficiencies (Finf) in individual homes is an important part of PM exposure assessment because individuals spend the majority of time indoors. While Finf of fine PM has most commonly been estimated using tracer species such as sulfur, here we evaluate an alternative that does not require particle collection, weighing and compositional analysis, and can be applied in situations with indoor sources of sulfur, such as environmental tobacco smoke, gas pilot lights, and humidifier use. This alternative method involves applying a recursive mass balance model (recursive model, RM) to continuous indoor and outdoor concentration measurements (e.g., light scattering data from nephelometers). We show that the RM can reliably estimate Finf, a crucial parameter for determining exposure to particles of outdoor origin. The RM Finf estimates showed good agreement with the conventional filter-based sulfur tracer approach. Our simulation results suggest that the RM Finf estimates are minimally impacted by measurement error. In addition, the average light scattering response per unit mass concentration was greater indoors than outdoors; after correcting for differences in light scattering response the median deviation from sulfur Finf was reduced from 15 to 11%. Thus, we have verified the RM applied to light scattering data. We show that the RM method is unable to provide satisfactory estimates of the individual components of Finf (penetration efficiency, air exchange rate, and deposition rate). However, this approach may allow Finf to be estimated in more residences, including those with indoor sources of sulfur. We show that individual homes vary in their infiltration efficiencies, thereby contributing to exposure misclassification in epidemiological studies that assign exposures using ambient monitoring data. This variation across homes indicates the need for home-specific estimation methods, such as the RM or sulfur tracer, instead of techniques that give average estimates of infiltration across homes.

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Abbreviations

a :

air exchange rate (h−1)

a 1 :

coefficient describing the infiltration, deposition, and exfiltration of ambient PM2.5

a 2 :

coefficient describing the deposition and exfiltration of indoor PM2.5

AVR:

sum of the absolute values of residuals

b sp :

particle light scattering coefficient

F inf :

infiltration efficiency

F inf RM :

infiltration efficiency obtained using nephelometer data and the recursive model approach

F inf S :

sulfur infiltration efficiency

HI:

Harvard impactor

k :

particle deposition rate (h−1)

Neph:

radiance research nephelometer

OLS:

ordinary least squares

P :

particle penetration efficiency

PM:

particulate matter

PM2.5:

particulate matter with an aerodynamic diameter less than 2.5 μm

RM:

recursive model

S i :

indoor sulfur concentration

S o :

outdoor sulfur concentration

XRF:

X-ray fluorescence

Φ :

total particle loss rate (h−1)

References

  • Abt E., Suh H.H., Catalano P., and Koutrakis P. Relative contribution of outdoor and indoor particle sources to indoor concentrations. Environ Sci Technol 2000: 34: 3579–3587.

    Article  CAS  Google Scholar 

  • Allen R., Larson T., Sheppard L., Wallace L., and Liu L.-J.S. Use of real-time light scattering data to estimate the contribution of infiltrated and indoor-generated particles to indoor air. Environ Sci Technol 2003: 37: 3484–3492.

    Article  CAS  Google Scholar 

  • Bennett D.H., and Koutrakis P. Determining the infiltration of outdoor particles in the indoor environment using a dynamic model. Aerosol Sci 2006: 37: 766–785.

    Article  CAS  Google Scholar 

  • Brauer M., Hirtle R., Lang B., and Ott W. Assessment of indoor fine aerosol contributions from environmental tobacco smoke and cooking with a portable nephelometer. J Expos Anal Environ Epidemiol 2000: 10: 136–144.

    Article  CAS  Google Scholar 

  • Dominici F., McDermott A., Zeger S.L., and Samet J.M. National maps of the effects of particulate matter on mortality: exploring geographical variation. Environ Health Perspect 2003: 111: 39–43.

    Article  Google Scholar 

  • Ebelt S.T., Wilson W.E., and Brauer M. Exposure to ambient and nonambient components of particulate matter – a comparison of health effects. Epidemiology 2005: 16: 396–405.

    Article  Google Scholar 

  • Geller M.D., Chang M.H., Sioutas C., Ostro B.D., and Lipsett M.J. Indoor/outdoor relationship and chemical composition of fine and coarse particles in the Southern California deserts. Atmos Environ 2002: 36: 1099–1110.

    Article  CAS  Google Scholar 

  • Horvath H., Kasahara M., and Pesava P. The size distribution and composition of the atmospheric aerosol at a rural and nearby urban location. J Aerosol Sci 1996: 27: 417–435.

    Article  CAS  Google Scholar 

  • Koenig J.Q., Mar T.F., Allen R.W., Jansen K., Lumley T., and Sullivan J.H., et al. Pulmonary effects of indoor- and outdoor-generated particles in children with asthma. Environ Health Perspect 2005: 113: 499–503.

    Article  CAS  Google Scholar 

  • Koutrakis P., Briggs S.L.K, and Leaderer B.P. Source apportionment of indoor aerosols in Suffolk and Onondaga Counties, New York. Environ Sci Technol 1992: 26: 521–527.

    Article  CAS  Google Scholar 

  • Lai A.C.K. Particle deposition indoors: a review. Indoor Air 2002: 12: 211–214.

    Article  CAS  Google Scholar 

  • Leaderer B.P., Naeher L., Jankun T., Balenger K., Holford T.R., and Toth C., et al. Indoor, outdoor, and regional summer and winter concentrations of PM10, PM2.5, SO42−, H+, NH4+, NO3−, NH3, and nitrous acid in homes with and without kerosene space heaters. Environ Health Perspect 1999: 107: 223–231.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Lewis C.W. Sources of air pollutants indoors: VOC and fine particulate species. J Expos Anal Environ Epidemiol 1991: 1: 31–44.

    CAS  Google Scholar 

  • Liu L.J.S., Box M., Kalman D., Kaufman J., Koenig J., and Larson T., et al. Exposure assessment of particulate matter for susceptible populations in Seattle. Environ Health Perspect 2003: 111: 909–918.

    Article  Google Scholar 

  • Liu L.J.S., Slaughter J.C., and Larson T.V. Comparison of light scattering devices and impactors for particulate measurements in indoor, outdoor, and personal environments. Environ Sci Technol 2002: 36: 2977–2986.

    Article  CAS  Google Scholar 

  • Long C.M., and Sarnat J.A. Indoor–outdoor relationships and infiltration behavior of elemental components of outdoor PM2.5 for Boston-area homes. Aerosol Sci Technol 2004: 38: 91–104.

    Article  CAS  Google Scholar 

  • Long C.M., Suh H.H., Catalano P.J., and Koutrakis P. Using time- and size-resolved particulate data to quantify indoor penetration and deposition behavior. Environ Sci Technol 2001: 35: 2089–2099.

    Article  CAS  Google Scholar 

  • Martuzevicius D., Grinshpun S.A., Reponen T., Gorny R.L., Shukla R., and Lockey J., et al. Spatial and temporal variations of PM2.5 concentration and composition throughout an urban area with high freeway density – the Greater Cincinnati Study. Atmos Environ 2004: 38: 1091–1105.

    Article  CAS  Google Scholar 

  • Meng Q.Y., Turpin B.J., Polidori A., Lee J.H., Weisel C., and Morandi M., et al. PM2.5 of ambient origin: estimates and exposure errors relevant to PM epidemiology. Environ Sci Technol 2005: 39: 5105–5112.

    Article  CAS  Google Scholar 

  • Mosley R.B., Greenwell D.J., Sparks L.E., Guo Z., Tucker W.G., and Fortmann R., et al. Penetration of ambient fine particles into the indoor environment. Aerosol Sci Technol 2001: 34: 127–136.

    Article  CAS  Google Scholar 

  • Na K.S., Sawant A.A., and Cocker D.R. Trace elements in fine particulate matter within a community in western riverside county, CA: focus on residential sites and a local high school. Atmos Environ 2004: 38: 2867–2877.

    Article  CAS  Google Scholar 

  • Nazaroff W.W., and Cass G.R. Mathematical modeling of indoor aerosol dynamics. Environ Sci Technol 1989: 23: 157–166.

    Article  CAS  Google Scholar 

  • Ott W., Wallace L., and Mage D. 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 

  • Ozkaynak H., Xue J., Weker R., Butler D., Koutrakis P., and Spengler J. The Particle Team (PTEAM) Study: Analysis of the Data, Final Report, Vol III, EPA/600/R-95/098. United States Environmental Protection Agency: Research Triangle Park, 1996.

    Google Scholar 

  • Peng R.D., Dominici F., Pastor-Barriuso R., Zeger S.L., and Samet J.M. Seasonal analyses of air pollution and mortality in 100 US cities. Am J Epidemiol 2005: 161: 585–594.

    Article  Google Scholar 

  • Sarnat J.A., Long C.M., Koutrakis P., Coull B.A., Schwartz J., and Suh H.H. Using sulfur as a tracer of outdoor fine particulate matter. Environ Sci Technol 2002: 36: 5305–5314.

    Article  CAS  Google Scholar 

  • Sarnat S.E., Coull B.A., Ruiz P.A., Koutrakis P., and Suh H.H. The influences of ambient particle composition and size on particle infiltration in Los Angeles, CA, residences. J Air Waste Manage Assoc 2006: 56: 186–196.

    Article  CAS  Google Scholar 

  • Smolik J., Zdimal V., Schwarz J., Lazaridis M., Havranek V., and Eleftheriadis K., et al. Size resolved mass concentration and elemental composition of atmospheric aerosols over the eastern Mediterranean area. Atmos Chem Phys 2003: 3: 2207–2216.

    Article  CAS  Google Scholar 

  • Suh H.H., Spengler J.D., and Koutrakis P. Personal exposures to acid aerosols and ammonia. Environ Sci Technol 1992: 26: 2507–2517.

    Article  CAS  Google Scholar 

  • Switzer P., and Ott W. Derivation of an indoor air averaging time model from the mass balance equation for the case of independent source inputs and fixed air exchange rates. J Exposure Anal Environ Epidemio 1992: 2 (Suppl 2): 113–135.

    Google Scholar 

  • Waggoner A.P., and Weiss R.E. Comparison of fine particle mass concentration and light scattering extinction in ambient aerosol. Atmos Environ 1980: 14: 623–626.

    Article  Google Scholar 

  • Wallace L., Williams R., Suggs J., and Jones P. Estimating Contributions of Outdoor Fine Particles to Indoor Concentrations and Personal Exposures: Effects of Household and Personal Activities. EPA/600/R-06/023. US EPA, National Exposure Research Laboratory: Research Triangle Park, NC, 2006.

    Google Scholar 

  • Williams R., Suggs J., Rea A., Sheldon L., Rodes C., and Thornburg J. The Research Triangle Park particulate matter panel study: modeling ambient source contribution to personal and residential PM mass concentrations. Atmos Environ 2003: 37: 5365–5378.

    Article  CAS  Google Scholar 

  • Wilson W.E., and Brauer M. Estimation of ambient and non-ambient components of particulate matter exposure from a personal monitoring panel study. J Expos Sci Environ Epidemiol 2006: 16: 264–274.

    Article  CAS  Google Scholar 

  • Wilson W.E., Mage D.T., and Grant L.D. 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 

  • Wu C.F., Jimenez J., Claiborn C., Gould T., Simpson C.D., Larson T., and Liu L.-J.S. Agricultural burning smoke in Eastern Washington: Part II. Exposure Assessment. Atmos Environ 2006: 40: 5379–5392.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We are grateful to the study participants for their willingness to participate in this study and the field technicians for collecting the data. This work was funded by the United States Environmental Protection Agency (EPA) through a cooperative agreement between the University of Washington and the EPA (CR82717701) and the EPA/UW Northwest Research Center for Particulate Air Pollution and Health (EPA Grant CR827355), and by the National Institute of Environmental Health Sciences (Grant No. P3OES07033). The views expressed in this paper do not necessarily reflect the views or policies of the EPA. Mention of trade names does not constitute endorsement or recommendation for use.

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Correspondence to Ryan Allen.

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Allen, R., Wallace, L., Larson, T. et al. Evaluation of the recursive model approach for estimating particulate matter infiltration efficiencies using continuous light scattering data. J Expo Sci Environ Epidemiol 17, 468–477 (2007). https://doi.org/10.1038/sj.jes.7500539

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