TY - JOUR
T1 - Determinants of indoor and personal exposure to PM2.5 of indoor and outdoor origin during the RIOPA study
AU - Meng, Qing Yu
AU - Spector, Dalia
AU - Colome, Steven
AU - Turpin, Barbara
N1 - Funding Information:
This research was supported by an EPA/NCEA-DOE/ORISE research fellowship (Q. Meng), the Health Effects Institute (#98-23-2), the Mickey Leland National Urban Air Toxics Center, the NIEHS Center of Excellence (ES05022), and the NJ Agricultural Experiment Station. Research was conducted, in part, under contract to the Health Effects Institute (HEI), an organization jointly funded by the United States Environmental Protection Agency (R828112) and automotive manufacturers. The contents of this article do not necessarily reflect the views of HEI nor the views and policies of EPA or of motor vehicle and engine manufacturers. The authors acknowledge the contributions of Dr. Dennis Kotchmar and RIOPA Investigators (Clifford Weisel, Maria Morandi, Jim Zhang, Thomas Stock, Arthur Winer), RIOPA study participants, and field and analytical personnel. Appendix
PY - 2009/11
Y1 - 2009/11
N2 - Effects of physical/environmental factors on fine particle (PM2.5) exposure, outdoor-to-indoor transport and air exchange rate (AER) were examined. The fraction of ambient PM2.5 found indoors (FINF) and the fraction to which people are exposed (α) modify personal exposure to ambient PM2.5. Because FINF, α, and AER are infrequently measured, some have used air conditioning (AC) as a modifier of ambient PM2.5 exposure. We found no single variable that was a good predictor of AER. About 50% and 40% of the variation in FINF and α, respectively, was explained by AER and other activity variables. AER alone explained 36% and 24% of the variations in FINF and α, respectively. Each other predictor, including Central AC Operation, accounted for less than 4% of the variation. This highlights the importance of AER measurements to predict FINF and α. Evidence presented suggests that outdoor temperature and home ventilation features affect particle losses as well as AER, and the effects differ. Total personal exposures to PM2.5 mass/species were reconstructed using personal activity and microenvironmental methods, and compared to direct personal measurement. Outdoor concentration was the dominant predictor of (partial R2 = 30-70%) and the largest contributor to (20-90%) indoor and personal exposures for PM2.5 mass and most species. Several activities had a dramatic impact on personal PM2.5 mass/species exposures for the few study participants exposed to or engaged in them, including smoking and woodworking. Incorporating personal activities (in addition to outdoor PM2.5) improved the predictive power of the personal activity model for PM2.5 mass/species; more detailed information about personal activities and indoor sources is needed for further improvement (especially for Ca, K, OC). Adequate accounting for particle penetration and persistence indoors and for exposure to non-ambient sources could potentially increase the power of epidemiological analyses linking health effects to particulate exposures.
AB - Effects of physical/environmental factors on fine particle (PM2.5) exposure, outdoor-to-indoor transport and air exchange rate (AER) were examined. The fraction of ambient PM2.5 found indoors (FINF) and the fraction to which people are exposed (α) modify personal exposure to ambient PM2.5. Because FINF, α, and AER are infrequently measured, some have used air conditioning (AC) as a modifier of ambient PM2.5 exposure. We found no single variable that was a good predictor of AER. About 50% and 40% of the variation in FINF and α, respectively, was explained by AER and other activity variables. AER alone explained 36% and 24% of the variations in FINF and α, respectively. Each other predictor, including Central AC Operation, accounted for less than 4% of the variation. This highlights the importance of AER measurements to predict FINF and α. Evidence presented suggests that outdoor temperature and home ventilation features affect particle losses as well as AER, and the effects differ. Total personal exposures to PM2.5 mass/species were reconstructed using personal activity and microenvironmental methods, and compared to direct personal measurement. Outdoor concentration was the dominant predictor of (partial R2 = 30-70%) and the largest contributor to (20-90%) indoor and personal exposures for PM2.5 mass and most species. Several activities had a dramatic impact on personal PM2.5 mass/species exposures for the few study participants exposed to or engaged in them, including smoking and woodworking. Incorporating personal activities (in addition to outdoor PM2.5) improved the predictive power of the personal activity model for PM2.5 mass/species; more detailed information about personal activities and indoor sources is needed for further improvement (especially for Ca, K, OC). Adequate accounting for particle penetration and persistence indoors and for exposure to non-ambient sources could potentially increase the power of epidemiological analyses linking health effects to particulate exposures.
KW - Air exchange rate
KW - Fine particle exposure
KW - PM
KW - PM and health
KW - Particle infiltration
UR - http://www.scopus.com/inward/record.url?scp=70949088781&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70949088781&partnerID=8YFLogxK
U2 - 10.1016/j.atmosenv.2009.07.066
DO - 10.1016/j.atmosenv.2009.07.066
M3 - Article
AN - SCOPUS:70949088781
SN - 1352-2310
VL - 43
SP - 5750
EP - 5758
JO - Atmospheric Environment
JF - Atmospheric Environment
IS - 36
ER -