Bayer-Oglesby, Lucy
Lade...
E-Mail-Adresse
Geburtsdatum
Projekt
Organisationseinheiten
Berufsbeschreibung
Nachname
Bayer-Oglesby
Vorname
Lucy
Name
Bayer-Oglesby, Lucy
2 Ergebnisse
Suchergebnisse
Gerade angezeigt 1 - 2 von 2
- PublikationCharacterization of source-specific air pollution exposure for a large population-based Swiss cohort (SAPALDIA)(National Institute of Environmental Health Sciences, 2007) Liu, L.-J. Sally; Curjuric, Ivan; Keidel, Dirk; Heldstab, Jürg; Künzli, Nino; Bayer-Oglesby, Lucy; Ackermann-Liebrich, Ursula; Schindler, Christian [in: Environmental Health Perspectives]Background: Although the dispersion model approach has been used in some epidemiologic studies to examine health effects of traffic-specific air pollution, no study has evaluated the model predictions vigorously. Methods: We evaluated total and traffic-specific particulate matter < 10 and < 2.5 microm in aero-dynamic diameter (PM(10), PM(2.5)), nitrogren dioxide, and nitrogen oxide concentrations predicted by Gaussian dispersion models against fixed-site measurements at different locations, including traffic-impacted, urban-background, and alpine settings between and across cities. The model predictions were then used to estimate individual subjects' historical and cumulative exposures with a temporal trend model. Results: Modeled PM(10) and NO(2) predicted at least 55% and 72% of the variability of the measured PM(10) and NO(2), respectively. Traffic-specific pollution estimates correlated with the NO(x) measurements (R(2) >or=0.77) for background sites but not for traffic sites. Regional background PM(10) accounted for most PM(10) mass in all cities. Whereas traffic PM(10) accounted for < 20% of the total PM(10), it varied significantly within cities. The modeling error for PM(10) was similar within and between cities. Traffic NO(x) accounted for the majority of NO(x) mass in urban areas, whereas background NO(x) accounted for the majority of NO(x) in rural areas. The within-city NO(2) modeling error was larger than that between cities. Conclusions: The dispersion model predicted well the total PM(10), NO(x), and NO(2) and traffic-specific pollution at background sites. However, the model underpredicted traffic NO(x) and NO(2) at traffic sites and needs refinement to reflect local conditions. The dispersion model predictions for PM(10) are suitable for examining individual exposures and health effects within and between cities.01A - Beitrag in wissenschaftlicher Zeitschrift
- PublikationLong-term Source-Specific Air Pollution Exposure Characterization for a Large Population-Based Swiss Cohort (SAPALDIA)(Lippincott Williams & Wilkins, 2006) Sally Liu, L J.; Curjuric, Ivan; Hazenkamp, Marianne; Keidel, Dirk; Bayer-Oglesby, Lucy; Ackermann-Liebrich, Ursula; Schindler, Christian [in: Epidemiology]Although evidence on acute health effects related to traffic exhaust is accumulating, there is less information regarding long-term exposure of source-specific air pollution in the general population. The SAPALDIA study is a long-term air pollution study that included 7990 subjects from 8 areas in Switzerland with the first health examination in 1991 and the second examination in 2002. Each area was monitored with up to 3 monitoring sites for PM, NO2, and other gaseous pollutants. In 1999–2000, a sampling campaign was conducted for PM10, PM2.5, and black smoke at 16 Swiss sites. In 2002–2003, passive NO2 measurements were collected strategically over the year outside and inside approximately 60 homes per area. Annual average concentrations of source-specific and total PM2.5, PM10, and NOx were estimated using a Gaussian dispersion model with GIS to match individual residences of the SAPALDIA subjects. This paper examines the performance of the dispersion model, variation of source-specific air pollution exposures, and the implications of these findings to long-term air pollution epidemiologic studies. For PM10 in 2000, modeled values predicted 68% of the variability in the measurements. For NO2 in 2000, the model predicted the measured values with an R2 over 0.80. The R2 for traffic-specific pollutant predictions ranged between 0.44 (P = 0.08) for traffic-related PM2.5 and 0.81 (P < 0.01) for traffic-related NO2 for sites with low traffic impacts. However, when traffic sites were included in the comparisons, the R2 was lower, ranging between 0.41 for traffic-originated PM10 and 0.51 for traffic-originated NO2. Nevertheless, our preliminary results indicated that variance in traffic-originated pollutants accounted for up to 45% of the variance in total PM10, 69% of that in total PM2.5, and 91% of that in NOx. In addition, we smoothed actual NO2 measurements outside individual residences and correlated the resulting smoothed estimates at these sites with NO2 estimates from the dispersion model. To obtain good agreement between the measured and modeled surfaces (r > 0.60), the minimal spatial smoothing window was found to range between 200 m in rural Davos and 1.75 km for urban Basel. Our results indicate that sites affected largely by regional and urban background pollution are properly presented by the model. Locations impacted by local traffic, however, may not be adequately predicted by the model and need either fine-tuning of the model or additional parameters to reflect local conditions. Predictions of exposures to source-specific air pollution are being examined against a series of respiratory and cardiovascular health effects in other papers.01A - Beitrag in wissenschaftlicher Zeitschrift