Journal article
Spatial PM2.5, NO2, O3 and BC models for Western Europe - Evaluation of spatiotemporal stability.
- de Hoogh K Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051 Basel, Switzerland; University of Basel, Petersplatz 1, Postfach, 4001 Basel, Switzerland. Electronic address: c.dehoogh@swisstph.ch.
- Chen J Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbrus 80125, 3508 TC Utrecht, the Netherlands. Electronic address: j.chen1@uu.nl.
- Gulliver J School of Geography, Geology and the Environment, University of Leicester, University Road, Leicester LE1 7RH, UK. Electronic address: jg435@leicester.ac.uk.
- Hoffmann B Institute for Occupational, Social and Environmental Medicine, Centre for Health and Society, Medical Faculty, Heinrich Heine University Düsseldorf, Universitätsstraße 1, 40225 Düsseldorf, Germany. Electronic address: b.hoffmann@uni-duesseldorf.de.
- Hertel O Department of Environmental Science, Aarhus University, 4000 Roskilde, Denmark. Electronic address: oh@envs.au.dk.
- Ketzel M Department of Environmental Science, Aarhus University, 4000 Roskilde, Denmark. Electronic address: mke@envs.au.dk.
- Bauwelinck M Interface Demography - Department of Sociology, Vrije Universiteit Brussel, Boulevard de la Plaine 2, 1050 Ixelles, Brussel, Belgium; Unit Health & Environment - Sciensano, Rue Juliette Wytsmanstraat 14, 1050, Brussels, Belgium. Electronic address: mariska.bauwelinck@vub.ac.be.
- van Donkelaar A Department of Physics and Atmospheric Science, Dalhousie University, B3H 4R2 Halifax, Nova Scotia, Canada. Electronic address: kelaar@Dal.Ca.
- Hvidtfeldt UA Danish Cancer Society Research Center, Strandboulevarden 49, 2100 Copenhagen, Denmark. Electronic address: ullah@cancer.dk.
- Katsouyanni K Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias Str, 115 27 Athens, Greece; Department Population Health Sciences, Department of Analytical, Environmental and Forensic Sciences, School of Population Health & Environmental Sciences, King's College Strand, London WC2R 2LS, UK. Electronic address: kkatsouy@med.uoa.gr.
- Klompmaker J Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbrus 80125, 3508 TC Utrecht, the Netherlands; National Institute for Public Health and the Environment (RIVM), Antonie van Leeuwenhoeklaan 9, 3721 MA Bilthoven, Netherlands. Electronic address: jochem.klompmaker@rivm.nl.
- Martin RV Department of Physics and Atmospheric Science, Dalhousie University, B3H 4R2 Halifax, Nova Scotia, Canada; Atomic and Molecular Physics Division, Harvard-Smithsonian Center for Astrophysics, 60 Garden St, Cambridge, MA 02138, United States of America. Electronic address: Randall.Martin@Dal.Ca.
- Samoli E Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias Str, 115 27 Athens, Greece. Electronic address: esamoli@med.uoa.gr.
- Schwartz PE Division of Environmental Medicine, Norwegian Institute of Public Health, PO Box 4404, Nydalen, N-0403 Oslo, Norway. Electronic address: Per.Schwarze@fhi.no.
- Stafoggia M Department of Epidemiology, Lazio Region Health Service/ASL, Roma 1, Via Cristoforo Colombo, 112 - 00147 Rome, Italy; Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden. Electronic address: m.stafoggia@deplazio.it.
- Bellander T Institute of Environmental Medicine, Karolinska Institutet, SE-171 77 Stockholm, Sweden. Electronic address: Tom.Bellander@ki.se.
- Strak M Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbrus 80125, 3508 TC Utrecht, the Netherlands. Electronic address: M.M.Strak@uu.nl.
- Wolf K Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Institute of Epidemiology, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany. Electronic address: kathrin.wolf@helmholtz-muenchen.de.
- Vienneau D Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051 Basel, Switzerland; University of Basel, Petersplatz 1, Postfach, 4001 Basel, Switzerland. Electronic address: danielle.vienneau@swisstph.ch.
- Brunekreef B Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbrus 80125, 3508 TC Utrecht, the Netherlands; Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, Netherlands. Electronic address: B.Brunekreef@uu.nl.
- Hoek G Institute for Risk Assessment Sciences (IRAS), Utrecht University, Postbrus 80125, 3508 TC Utrecht, the Netherlands. Electronic address: G.Hoek@uu.nl.
- 2018-08-04
Published in:
- Environment international. - 2018
Black carbon
LUR
NO(2)
Ozone
PM(2.5)
Spatiotemporal stability
Air Pollutants
Environmental Monitoring
Europe
Models, Statistical
Particulate Matter
Spatio-Temporal Analysis
English
BACKGROUND
In order to investigate associations between air pollution and adverse health effects consistent fine spatial air pollution surfaces are needed across large areas to provide cohorts with comparable exposures. The aim of this paper is to develop and evaluate fine spatial scale land use regression models for four major health relevant air pollutants (PM2.5, NO2, BC, O3) across Europe.
METHODS
We developed West-European land use regression models (LUR) for 2010 estimating annual mean PM2.5, NO2, BC and O3 concentrations (including cold and warm season estimates for O3). The models were based on AirBase routine monitoring data (PM2.5, NO2 and O3) and ESCAPE monitoring data (BC), and incorporated satellite observations, dispersion model estimates, land use and traffic data. Kriging was performed on the residual spatial variation from the LUR models and added to the exposure estimates. One model was developed using all sites (100%). Robustness of the models was evaluated by performing a five-fold hold-out validation and for PM2.5 and NO2 additionally with independent comparison at ESCAPE measurements. To evaluate the stability of each model's spatial structure over time, separate models were developed for different years (NO2 and O3: 2000 and 2005; PM2.5: 2013).
RESULTS
The PM2.5, BC, NO2, O3 annual, O3 warm season and O3 cold season models explained respectively 72%, 54%, 59%, 65%, 69% and 83% of spatial variation in the measured concentrations. Kriging proved an efficient technique to explain a part of residual spatial variation for the pollutants with a strong regional component explaining respectively 10%, 24% and 16% of the R2 in the PM2.5, O3 warm and O3 cold models. Explained variance at fully independent sites vs the internal hold-out validation was slightly lower for PM2.5 (65% vs 66%) and lower for NO2 (49% vs 57%). Predictions from the 2010 model correlated highly with models developed in other years at the overall European scale.
CONCLUSIONS
We developed robust PM2.5, NO2, O3 and BC hybrid LUR models. At the West-European scale models were robust in time, becoming less robust at smaller spatial scales. Models were applied to 100 × 100 m surfaces across Western Europe to allow for exposure assignment for 35 million participants from 18 European cohorts participating in the ELAPSE study.
In order to investigate associations between air pollution and adverse health effects consistent fine spatial air pollution surfaces are needed across large areas to provide cohorts with comparable exposures. The aim of this paper is to develop and evaluate fine spatial scale land use regression models for four major health relevant air pollutants (PM2.5, NO2, BC, O3) across Europe.
METHODS
We developed West-European land use regression models (LUR) for 2010 estimating annual mean PM2.5, NO2, BC and O3 concentrations (including cold and warm season estimates for O3). The models were based on AirBase routine monitoring data (PM2.5, NO2 and O3) and ESCAPE monitoring data (BC), and incorporated satellite observations, dispersion model estimates, land use and traffic data. Kriging was performed on the residual spatial variation from the LUR models and added to the exposure estimates. One model was developed using all sites (100%). Robustness of the models was evaluated by performing a five-fold hold-out validation and for PM2.5 and NO2 additionally with independent comparison at ESCAPE measurements. To evaluate the stability of each model's spatial structure over time, separate models were developed for different years (NO2 and O3: 2000 and 2005; PM2.5: 2013).
RESULTS
The PM2.5, BC, NO2, O3 annual, O3 warm season and O3 cold season models explained respectively 72%, 54%, 59%, 65%, 69% and 83% of spatial variation in the measured concentrations. Kriging proved an efficient technique to explain a part of residual spatial variation for the pollutants with a strong regional component explaining respectively 10%, 24% and 16% of the R2 in the PM2.5, O3 warm and O3 cold models. Explained variance at fully independent sites vs the internal hold-out validation was slightly lower for PM2.5 (65% vs 66%) and lower for NO2 (49% vs 57%). Predictions from the 2010 model correlated highly with models developed in other years at the overall European scale.
CONCLUSIONS
We developed robust PM2.5, NO2, O3 and BC hybrid LUR models. At the West-European scale models were robust in time, becoming less robust at smaller spatial scales. Models were applied to 100 × 100 m surfaces across Western Europe to allow for exposure assignment for 35 million participants from 18 European cohorts participating in the ELAPSE study.
- Language
-
- English
- Open access status
- closed
- Identifiers
-
- DOI 10.1016/j.envint.2018.07.036
- PMID 30075373
- Persistent URL
- https://folia.unifr.ch/global/documents/84166
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