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

• Published in: Journal of exposure science & environmental epidemiology Vol. 17; no. 5; pp. 468 - 477
• Main Authors: Allen, Ryan, Wallace, Lance, Larson, Timothy, Sheppard, Lianne, Liu, Lee-Jane Sally
• Format: Journal Article
• Language: English
• Published: United States Nature Publishing Group 01.08.2007
• Subjects: Air Pollution - analysis; Analysis; Computer Simulation; Data Collection; Environmental Monitoring - methods; Epidemiology; Error analysis; Estimates; Estimation; Evaluation; Exposure; Filtration - methods; Health aspects; Humans; Indoor air pollution; Indoor environments; Infiltration; Light scattering; Light-scattering photometry; Mass balance; Models, Biological; Nephelometers; Particle Size; Particles; Particulate emissions; Particulate matter; Particulate Matter - analysis; Particulate Matter - toxicity; Passive smoking; Public Health; Risk Assessment; Scattering, Radiation; Sulfur; Sulfur - analysis; Sulfur - toxicity; Time Factors; Tobacco; United States
• ISSN: 1559-0631; 1559-064X
• DOI: 10.1038/sj.jes.7500539

Quantifying particulate matter (PM) infiltration efficiencies (F(inf)) in individual homes is an important part of PM exposure assessment because individuals spend the majority of time indoors. While F(inf) 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 F(inf), a crucial parameter for determining exposure to particles of outdoor origin. The RM F(inf) estimates showed good agreement with the conventional filter-based sulfur tracer approach. Our simulation results suggest that the RM F(inf) 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 F(inf) 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 F(inf) (penetration efficiency, air exchange rate, and deposition rate). However, this approach may allow F(inf) 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.