Details

Autor(en) / Beteiligte

• Meng, Qing Yu
• Turpin, Barbara J
• Polidori, Andrea
• Lee, Jong Hoon
• Weisel, Clifford
• Morandi, Maria
• Colome, Steven
• Stock, Thomas
• Winer, Arthur
• Zhang, Jenfeng (Jim)

Titel

PM2.5 of Ambient Origin:  Estimates and Exposure Errors Relevant to PM Epidemiology

Ist Teil von

• Environmental science & technology, 2005-07, Vol.39 (14), p.5105-5112

Ort / Verlag

Washington, DC: American Chemical Society

Erscheinungsjahr

2005

Quelle

MEDLINE

Beschreibungen/Notizen

• Epidemiological studies routinely use central-site particulate matter (PM) as a surrogate for exposure to PM of ambient (outdoor) origin. Below we quantify exposure errors that arise from variations in particle infiltration to aid evaluation of the use of this surrogate, rather than actual exposure, in PM epidemiology. Measurements from 114 homes in three cities from the Relationship of Indoor, Outdoor and Personal Air (RIOPA) study were used. Indoor PM2.5 of outdoor origin was calculated as follows:  (1) assuming a constant infiltration factor, as would be the case if central-site PM were a “perfect surrogate” for exposure to outdoor particles; (2) including variations in measured air exchange rates across homes; (3) also incorporating home-to-home variations in particle composition, and (4) calculating sample-specific infiltration factors. The final estimates of PM2.5 of outdoor origin take into account variations in building construction, ventilation practices, and particle properties that result in home-to-home and day-to-day variations in particle infiltration. As assumptions became more realistic (from the first, most constrained model to the fourth, least constrained model), the mean concentration of PM2.5 of outdoor origin increased. Perhaps more importantly, the bandwidth of the distribution increased. These results quantify several ways in which the use of central site PM results in underestimates of the ambient PM2.5 exposure distribution bandwidth. The result is larger uncertainties in relative risk factors for PM2.5 than would occur if epidemiological studies used more accurate exposure measures. In certain situations this can lead to bias.