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Development of land use regression models for nitrogen dioxide, ultrafine particles, lung deposited surface area, and four other markers of particulate matter pollution in the Swiss SAPALDIA regions

Eeftens, Marloes and Meier, Reto and Schindler, Christian and Aguilera, Inmaculada and Phuleria, Harish and Ineichen, Alex and Davey, Mark and Ducret-Stich, Regina and Keidel, Dirk and Probst-Hensch, Nicole and Künzli, Nino and Tsai, Ming-Yi. (2016) Development of land use regression models for nitrogen dioxide, ultrafine particles, lung deposited surface area, and four other markers of particulate matter pollution in the Swiss SAPALDIA regions. Environmental health, 15. p. 53.

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Official URL: http://edoc.unibas.ch/43544/

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Abstract

Land Use Regression (LUR) is a popular method to explain and predict spatial contrasts in air pollution concentrations, but LUR models for ultrafine particles, such as particle number concentration (PNC) are especially scarce. Moreover, no models have been previously presented for the lung deposited surface area (LDSA) of ultrafine particles. The additional value of ultrafine particle metrics has not been well investigated due to lack of exposure measurements and models.; Air pollution measurements were performed in 2011 and 2012 in the eight areas of the Swiss SAPALDIA study at up to 40 sites per area for NO2 and at 20 sites in four areas for markers of particulate air pollution. We developed multi-area LUR models for biannual average concentrations of PM2.5, PM2.5 absorbance, PM10, PMcoarse, PNC and LDSA, as well as alpine, non-alpine and study area specific models for NO2, using predictor variables which were available at a national level. Models were validated using leave-one-out cross-validation, as well as independent external validation with routine monitoring data.; Model explained variance (R(2)) was moderate for the various PM mass fractions PM2.5 (0.57), PM10 (0.63) and PMcoarse (0.45), and was high for PM2.5 absorbance (0.81), PNC (0.87) and LDSA (0.91). Study-area specific LUR models for NO2 (R(2) range 0.52-0.89) outperformed combined-area alpine (R (2) = 0.53) and non-alpine (R (2) = 0.65) models in terms of both cross-validation and independent external validation, and were better able to account for between-area variability. Predictor variables related to traffic and national dispersion model estimates were important predictors.; LUR models for all pollutants captured spatial variability of long-term average concentrations, performed adequately in validation, and could be successfully applied to the SAPALDIA cohort. Dispersion model predictions or area indicators served well to capture the between area variance. For NO2, applying study-area specific models was preferable over applying combined-area alpine/non-alpine models. Correlations between pollutants were higher in the model predictions than in the measurements, so it will remain challenging to disentangle their health effects.
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH)
09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH) > Department of Epidemiology and Public Health (EPH) > Chronic Disease Epidemiology > Air Pollution and Health (Künzli)
03 Faculty of Medicine > Departement Public Health > Sozial- und Präventivmedizin > Air Pollution and Health (Künzli)
UniBasel Contributors:Eeftens, Marloes and Schindler, Christian and Phuleria, Harish Chandra and Ineichen, Alex and Ducret-Stich, Regina and Keidel, Dirk and Probst Hensch, Nicole and Künzli, Nino and Tsai, Ming
Item Type:Article, refereed
Publisher:BioMed Central
ISSN:1476-069X
Note:Publication type according to Uni Basel Research Database: Journal article
Identification Number:
Last Modified:30 Aug 2016 09:39
Deposited On:30 Aug 2016 09:39

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