Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/109144
Title: Land Use Regression Models for Ultrafine Particles in Six European Areas
Author: Nunen, Erik van
Vermeulen, Roel
Tsai, Ming-Yi
Probst-Hensch, Nicole M.
Ineichen, Alex
Davey, Mark
Imboden, Medea
Ducret Stich, Regina
Naccarati, Alessio
Raffaele, Daniela
Ranzi, Andrea
Ivaldi, Cristiana
Galassi, Claudia
Nieuwenhuijsen, Mark J.
Curto, Ariadna
Donaire González, David
Cirach, Marta
Chatzi, Leda
Kampouri, Mariza
Vlaanderen, Jelle
Meliefste, Kees
Buijtenhuijs, Daan
Brunekreef, Bert
Morley, David
Vineis, Paolo
Gulliver, John
Hoek, Gerard
Keywords: Partícules (Matèria)
Anàlisi de regressió
Particles
Regression analysis
Issue Date: 28-Feb-2017
Publisher: ACS
Abstract: Long-term ultrafine particle (UFP) exposure estimates at a fine spatial scale are needed for epidemiological studies. Land use regression (LUR) models were developed and evaluated for six European areas based on repeated 30 min monitoring following standardized protocols. In each area; Basel (Switzerland), Heraklion (Greece), Amsterdam, Maastricht, and Utrecht ("The Netherlands"), Norwich (United Kingdom), Sabadell (Spain), and Turin (Italy), 160-240 sites were monitored to develop LUR models by supervised stepwise selection of GIS predictors. For each area and all areas combined, 10 models were developed in stratified random selections of 90% of sites. UFP prediction robustness was evaluated with the intraclass correlation coefficient (ICC) at 31-50 external sites per area. Models from Basel and The Netherlands were validated against repeated 24 h outdoor measurements. Structure and model R2 of local models were similar within, but varied between areas (e.g., 38-43% Turin; 25-31% Sabadell). Robustness of predictions within areas was high (ICC 0.73-0.98). External validation R2 was 53% in Basel and 50% in The Netherlands. Combined area models were robust (ICC 0.93-1.00) and explained UFP variation almost equally well as local models. In conclusion, robust UFP LUR models could be developed on short-term monitoring, explaining around 50% of spatial variance in longer-term measurements.
Note: Reproducció del document publicat a: http://dx.doi.org/10.1021/acs.est.6b05920
It is part of: Environmental Science & Technology, 2017, vol. 51, num. 6, p. 3336-3345
Related resource: http://dx.doi.org/10.1021/acs.est.6b05920
URI: http://hdl.handle.net/2445/109144
ISSN: 0013-936X
Appears in Collections:Articles publicats en revistes (ISGlobal)

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