TY - JOUR
T1 - Development of land use regression models for elemental, organic carbon, PAH, and hopanes/steranes in 10 ESCAPE/TRANSPHORM European study areas
AU - Jedynska, Aleksandra
AU - Hoek, Gerard
AU - Wang, Meng
AU - Eeftens, Marloes
AU - Cyrys, Josef
AU - Keuken, Menno
AU - Ampe, Christophe
AU - Beelen, Rob
AU - Cesaroni, Giulia
AU - Forastiere, Francesco
AU - Cirach, Marta
AU - De Hoogh, Kees
AU - De Nazelle, Audrey
AU - Nystad, Wenche
AU - Declercq, Christophe
AU - Eriksen, Kirsten T.
AU - Dimakopoulou, Konstantina
AU - Lanki, Timo
AU - Meliefste, Kees
AU - Nieuwenhuijsen, Mark J.
AU - Yli-Tuomi, Tarja
AU - Raaschou-Nielsen, Ole
AU - Brunekreef, Bert
AU - Kooter, Ingeborg M.
N1 - Publisher Copyright:
© 2014 American Chemical Society.
PY - 2014/12/16
Y1 - 2014/12/16
N2 - Land use regression (LUR) models have been used to model concentrations of mainly traffic-related air pollutants (nitrogen oxides (NOx), particulate matter (PM) mass or absorbance). Few LUR models are published of PM composition, whereas the interest in health effects related to particle composition is increasing. The aim of our study was to evaluate LUR models of polycyclic aromatic hydrocarbons (PAH), hopanes/steranes, and elemental and organic carbon (EC/OC) content of PM2.5. In 10 European study areas, PAH, hopanes/steranes, and EC/OC concentrations were measured at 16-40 sites per study area. LUR models for each study area were developed on the basis of annual average concentrations and predictor variables including traffic, population, industry, natural land obtained from geographic information systems. The highest median model explained variance (R2) was found for EC - 84%. The median R2 was 51% for OC, 67% for benzo[a]pyrene, and 38% for sum of hopanes/steranes, with large variability between study areas. Traffic predictors were included in most models. Population and natural land were included frequently as additional predictors. The moderate to high explained variance of LUR models and the overall moderate correlation with PM2.5 model predictions support the application of especially the OC and PAH models in epidemiological studies.
AB - Land use regression (LUR) models have been used to model concentrations of mainly traffic-related air pollutants (nitrogen oxides (NOx), particulate matter (PM) mass or absorbance). Few LUR models are published of PM composition, whereas the interest in health effects related to particle composition is increasing. The aim of our study was to evaluate LUR models of polycyclic aromatic hydrocarbons (PAH), hopanes/steranes, and elemental and organic carbon (EC/OC) content of PM2.5. In 10 European study areas, PAH, hopanes/steranes, and EC/OC concentrations were measured at 16-40 sites per study area. LUR models for each study area were developed on the basis of annual average concentrations and predictor variables including traffic, population, industry, natural land obtained from geographic information systems. The highest median model explained variance (R2) was found for EC - 84%. The median R2 was 51% for OC, 67% for benzo[a]pyrene, and 38% for sum of hopanes/steranes, with large variability between study areas. Traffic predictors were included in most models. Population and natural land were included frequently as additional predictors. The moderate to high explained variance of LUR models and the overall moderate correlation with PM2.5 model predictions support the application of especially the OC and PAH models in epidemiological studies.
UR - https://www.scopus.com/pages/publications/84918591214
U2 - 10.1021/es502568z
DO - 10.1021/es502568z
M3 - Article
C2 - 25317817
AN - SCOPUS:84918591214
SN - 0013-936X
VL - 48
SP - 14435
EP - 14444
JO - Environmental Science and Technology
JF - Environmental Science and Technology
IS - 24
ER -