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Land Use Regression Models for Ultrafine Particles in Six European Areas

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Title: Land Use Regression Models for Ultrafine Particles in Six European Areas
Authors: Van Nunen, E
Vermeulen, R
Tsai, M-Y
Probst-Hensch, N
Ineichen, A
Davey, M
Imboden, M
Ducret-Stich, R
Naccarati, A
Raffaele, D
Ranzi, A
Ivaldi, C
Galassi, C
Nieuwenhuijsen, M
Curto, A
Donaire-Gonzalez, D
Cirach, M
Chatzi, L
Kampouri, M
Vlaanderen, J
Meliefste, K
Buijtenhuijs, D
Brunekreef, B
Morley, D
Vineis, P
Gulliver, J
Hoek, G
Item Type: Journal Article
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.
Issue Date: 28-Feb-2017
Date of Acceptance: 28-Feb-2017
URI: http://hdl.handle.net/10044/1/46082
DOI: https://dx.doi.org/10.1021/acs.est.6b05920
ISSN: 0013-936X
Publisher: American Chemical Society
Start Page: 3336
End Page: 3345
Journal / Book Title: ENVIRONMENTAL SCIENCE & TECHNOLOGY
Volume: 51
Issue: 6
Copyright Statement: © 2017 American Chemical Society. This is an open access article published under a Creative Commons Non-Commercial No Derivative Works (CC-BY-NC-ND http://pubs.acs.org/page/policy/authorchoice_ccbyncnd_termsofuse.html) Attribution License, which permits copying and redistribution of the article, and creation of adaptations, all for non-commercial purposes.
Sponsor/Funder: Commission of the European Communities
Funder's Grant Number: 308610
Keywords: Science & Technology
Technology
Life Sciences & Biomedicine
Engineering, Environmental
Environmental Sciences
Engineering
Environmental Sciences & Ecology
BLACK CARBON
PARTICULATE MATTER
SPATIAL VARIATION
INTERNATIONAL AIRPORT
NUMBER CONCENTRATIONS
PM2.5 ABSORBENCY
NITROGEN-DIOXIDE
ESCAPE PROJECT
AIR-POLLUTION
NO2
MD Multidisciplinary
Publication Status: Published
Appears in Collections:Faculty of Medicine
Epidemiology, Public Health and Primary Care



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