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Land Use Regression Models for Ultrafine Particles in Six European Areas.pdf (1.57 MB)

Land Use Regression Models for Ultrafine Particles in Six European Areas.

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posted on 2019-06-28, 14:54 authored by E van Nunen, R Vermeulen, M-Y Tsai, N Probst-Hensch, A Ineichen, M Davey, M Imboden, R Ducret-Stich, A Naccarati, D Raffaele, A Ranzi, C Ivaldi, C Galassi, M Nieuwenhuijsen, A Curto, D Donaire-Gonzalez, M Cirach, L Chatzi, M Kampouri, J Vlaanderen, K Meliefste, D Buijtenhuijs, B Brunekreef, D Morley, P Vineis, J Gulliver, G Hoek
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.

Funding

This work was funded by the EU seventh Framework Program EXPOSOMICS Project. Grant Agreement No.: 308610, and the Compagnia di San Paolo (Turin, Italy) to Paolo Vineis. We are very grateful to the following people for their contribution: Jules Kerckhoffs, Cristina Vert Roca, Annemarie Melis, Andreas Schwärzler, Gregor Juretzko, Katja Stähli, Sandra Okorga, Benjamin Flueckiger, Lourdes Arjona, Pau Pañella, Danai Dafni, and Minas Iak. We thank Maastricht University and the municipality of Amsterdam for using their facilities during the short-term monitoring campaigns.

History

Citation

Environmental Science and Technology, 2017, 51 (6), pp. 3336-3345

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/School of Geography, Geology and the Environment

Version

  • VoR (Version of Record)

Published in

Environmental Science and Technology

Publisher

American Chemical Society

eissn

1520-5851

Acceptance date

2017-02-28

Copyright date

2017

Available date

2019-06-28

Publisher version

https://pubs.acs.org/doi/10.1021/acs.est.6b05920

Notes

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.6b05920. 1. Study areas; 2. Co-location of UFP monitors; 3. Imputing missing Reference Site UFP concentrations; 4. GIS predictors for Land Use Regression Modeling; 5. Local and combined area LUR models; 6. Robustness of predicted UFP concentrations; 7. Models developed upon 100% of the sites; 8. Mixed-Effect Models Combined area LUR models; 9. Leave One Area Out combined models (PDF)

Language

en

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