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Environ Sci Technol. 2017 Mar 21;51(6):3336-3345. doi: 10.1021/acs.est.6b05920. Epub 2017 Mar 13.

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

Author information

1
Institute for Risk Assessment Sciences (IRAS), division of Environmental Epidemiology (EEPI), Utrecht University , Utrecht, The Netherlands.
2
Swiss Tropical and Public Health (TPH) Institute, University of Basel , Basel, Switzerland.
3
University of Basel , Basel, Switzerland.
4
Department of Environmental and Occupational Health Sciences, University of Washington , Seattle, Washington United States.
5
Human Genetics Foundation , Turin, Italy.
6
Environmental Health Reference Centre, Regional Agency for Prevention, Environment and Energy of Emilia-Romagna, Modena, Italy.
7
ARPA Piemonte, Turin, Italy.
8
Unit of Cancer Epidemiology, Citta' della Salute e della Scienza University Hospital and Centre for Cancer Prevention, Turin, Italy.
9
ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain.
10
Department of Experimental and Health Sciences, Pompeu Fabra University (UPF) , Barcelona, Spain.
11
CIBER Epidemiologia y Salud Pública (CIBERESP), Barcelona, Spain.
12
Department of Social Medicine, University of Crete , Heraklion, Greece.
13
MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London , St Mary's Campus, London, United Kingdom.

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.

PMID:
28244744
PMCID:
PMC5362744
DOI:
10.1021/acs.est.6b05920
[Indexed for MEDLINE]
Free PMC Article

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