Development of land use regression models for particle composition in twenty study areas in Europe

Kees De Hoogh; Meng Wang; Martin Adam; Chiara Badaloni; Rob Beelen; Matthias Birk; Giulia Cesaroni; Marta Cirach; Christophe Declercq; Audrius Dědelě; Evi Dons; Audrey De Nazelle; Marloes Eeftens; Kirsten Eriksen; Charlotta Eriksson; Paul Fischer; Regina Gražulevičieně; Alexandros Gryparis; Barbara Hoffmann; Michael Jerrett; Klea Katsouyanni; Minas Iakovides; Timo Lanki; Sarah Lindley; Christian Madsen; Anna Mölter; Gioia Mosler; Gizella Nádor; Mark Nieuwenhuijsen; Göran Pershagen; Annette Peters; Harisch Phuleria; Nicole Probst-Hensch; Ole Raaschou-Nielsen; Ulrich Quass; Andrea Ranzi; Euripides Stephanou; Dorothea Sugiri; Per Schwarze; Ming Yi Tsai; Tarja Yli-Tuomi; Mihály J. Varró; Danielle Vienneau; Gudrun Weinmayr; Bert Brunekreef; Gerard Hoek

doi:10.1021/es400156t

Development of land use regression models for particle composition in twenty study areas in Europe

Kees De Hoogh
, Meng Wang
, Martin Adam
, Chiara Badaloni
, Rob Beelen
, Matthias Birk
, Giulia Cesaroni
, Marta Cirach
, Christophe Declercq
, Audrius Dědelě
, Evi Dons
, Audrey De Nazelle
, Marloes Eeftens
, Kirsten Eriksen
, Charlotta Eriksson
, Paul Fischer
, Regina Gražulevičieně
, Alexandros Gryparis
, Barbara Hoffmann
, Michael Jerrett

Klea Katsouyanni, Minas Iakovides, Timo Lanki, Sarah Lindley, Christian Madsen, Anna Mölter, Gioia Mosler, Gizella Nádor, Mark Nieuwenhuijsen, Göran Pershagen, Annette Peters, Harisch Phuleria, Nicole Probst-Hensch, Ole Raaschou-Nielsen, Ulrich Quass, Andrea Ranzi, Euripides Stephanou, Dorothea Sugiri, Per Schwarze, Ming Yi Tsai, Tarja Yli-Tuomi, Mihály J. Varró, Danielle Vienneau, Gudrun Weinmayr, Bert Brunekreef, Gerard Hoek

Department of Epidemiology and Environmental Health

University at Buffalo

Imperial College London
Swiss Tropical and Public Health Institute
University of Basel
Department of Epidemiology Lazio Regional Health Service
Utrecht University
HMGU Institute of Epidemiology I
(ISGlobal) Instituto de Salud Global de Barcelona
Hospital del Mar
Centro de Investigación Biomédicaen Red de Epidemiología y Salud Pública (CIBERESP)
French Institute for Public Health Surveillance
Vytautas Magnus University
Flemish Institute for Technological Research
Hasselt University
Danish Cancer Society
Karolinska Institutet
National Institute of Public Health and the Environment
Heinrich Heine University Düsseldorf
National and Kapodistrian University of Athens
University of California at Berkeley
University of Crete
National Institute for Health and Welfare
University of Manchester
Norwegian Institute of Public Health
Hungarian National Institute of Environmental Health
HMGU Institute of Epidemiology II
Air Quality and Sustainable Nanotechnology
ARPA Emilia Romagna
University of Washington

Research output: Contribution to journal › Article › peer-review

201 Scopus citations

Abstract

Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO₂), nitrogen oxides (NO_x) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM₁₀ and PM_2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed. Good models were developed for Cu, Fe, and Zn in both fractions (PM₁₀ and PM _2.5) explaining on average between 67 and 79% of the concentration variance (R²) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM₁₀ and PM_2.5 fractions and Si, Ni, and K in the PM₁₀ fraction performed moderately with R² ranging from 50 to 61%. Si, NI, and K models for PM_2.5 performed poorest with R² under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE.

Original language	English
Pages (from-to)	5778-5786
Number of pages	9
Journal	Environmental Science and Technology
Volume	47
Issue number	11
DOIs	https://doi.org/10.1021/es400156t
State	Published - Jun 4 2013

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10.1021/es400156t

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De Hoogh, K., Wang, M., Adam, M., Badaloni, C., Beelen, R., Birk, M., Cesaroni, G., Cirach, M., Declercq, C., Dědelě, A., Dons, E., De Nazelle, A., Eeftens, M., Eriksen, K., Eriksson, C., Fischer, P., Gražulevičieně, R., Gryparis, A., Hoffmann, B., ... Hoek, G. (2013). Development of land use regression models for particle composition in twenty study areas in Europe. Environmental Science and Technology, 47(11), 5778-5786. https://doi.org/10.1021/es400156t

@article{3bfcb62b1dc0498594845226bd2e65a3,

title = "Development of land use regression models for particle composition in twenty study areas in Europe",

abstract = "Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM10 and PM2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed. Good models were developed for Cu, Fe, and Zn in both fractions (PM10 and PM 2.5) explaining on average between 67 and 79\% of the concentration variance (R2) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM10 and PM2.5 fractions and Si, Ni, and K in the PM10 fraction performed moderately with R2 ranging from 50 to 61\%. Si, NI, and K models for PM2.5 performed poorest with R2 under 50\%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE.",

author = "\{De Hoogh\}, Kees and Meng Wang and Martin Adam and Chiara Badaloni and Rob Beelen and Matthias Birk and Giulia Cesaroni and Marta Cirach and Christophe Declercq and Audrius D{\v e}del{\v e} and Evi Dons and \{De Nazelle\}, Audrey and Marloes Eeftens and Kirsten Eriksen and Charlotta Eriksson and Paul Fischer and Regina Gra{\v z}ulevi{\v c}ien{\v e} and Alexandros Gryparis and Barbara Hoffmann and Michael Jerrett and Klea Katsouyanni and Minas Iakovides and Timo Lanki and Sarah Lindley and Christian Madsen and Anna M{\"o}lter and Gioia Mosler and Gizella N{\'a}dor and Mark Nieuwenhuijsen and G{\"o}ran Pershagen and Annette Peters and Harisch Phuleria and Nicole Probst-Hensch and Ole Raaschou-Nielsen and Ulrich Quass and Andrea Ranzi and Euripides Stephanou and Dorothea Sugiri and Per Schwarze and Tsai, \{Ming Yi\} and Tarja Yli-Tuomi and Varr{\'o}, \{Mih{\'a}ly J.\} and Danielle Vienneau and Gudrun Weinmayr and Bert Brunekreef and Gerard Hoek",

year = "2013",

month = jun,

day = "4",

doi = "10.1021/es400156t",

language = "English",

volume = "47",

pages = "5778--5786",

journal = "Environmental Science and Technology",

issn = "0013-936X",

publisher = "American Chemical Society",

number = "11",

}

De Hoogh, K, Wang, M, Adam, M, Badaloni, C, Beelen, R, Birk, M, Cesaroni, G, Cirach, M, Declercq, C, Dědelě, A, Dons, E, De Nazelle, A, Eeftens, M, Eriksen, K, Eriksson, C, Fischer, P, Gražulevičieně, R, Gryparis, A, Hoffmann, B, Jerrett, M, Katsouyanni, K, Iakovides, M, Lanki, T, Lindley, S, Madsen, C, Mölter, A, Mosler, G, Nádor, G, Nieuwenhuijsen, M, Pershagen, G, Peters, A, Phuleria, H, Probst-Hensch, N, Raaschou-Nielsen, O, Quass, U, Ranzi, A, Stephanou, E, Sugiri, D, Schwarze, P, Tsai, MY, Yli-Tuomi, T, Varró, MJ, Vienneau, D, Weinmayr, G, Brunekreef, B & Hoek, G 2013, 'Development of land use regression models for particle composition in twenty study areas in Europe', Environmental Science and Technology, vol. 47, no. 11, pp. 5778-5786. https://doi.org/10.1021/es400156t

TY - JOUR

T1 - Development of land use regression models for particle composition in twenty study areas in Europe

AU - De Hoogh, Kees

AU - Wang, Meng

AU - Adam, Martin

AU - Badaloni, Chiara

AU - Beelen, Rob

AU - Birk, Matthias

AU - Cesaroni, Giulia

AU - Cirach, Marta

AU - Declercq, Christophe

AU - Dědelě, Audrius

AU - Dons, Evi

AU - De Nazelle, Audrey

AU - Eeftens, Marloes

AU - Eriksen, Kirsten

AU - Eriksson, Charlotta

AU - Fischer, Paul

AU - Gražulevičieně, Regina

AU - Gryparis, Alexandros

AU - Hoffmann, Barbara

AU - Jerrett, Michael

AU - Katsouyanni, Klea

AU - Iakovides, Minas

AU - Lanki, Timo

AU - Lindley, Sarah

AU - Madsen, Christian

AU - Mölter, Anna

AU - Mosler, Gioia

AU - Nádor, Gizella

AU - Nieuwenhuijsen, Mark

AU - Pershagen, Göran

AU - Peters, Annette

AU - Phuleria, Harisch

AU - Probst-Hensch, Nicole

AU - Raaschou-Nielsen, Ole

AU - Quass, Ulrich

AU - Ranzi, Andrea

AU - Stephanou, Euripides

AU - Sugiri, Dorothea

AU - Schwarze, Per

AU - Tsai, Ming Yi

AU - Yli-Tuomi, Tarja

AU - Varró, Mihály J.

AU - Vienneau, Danielle

AU - Weinmayr, Gudrun

AU - Brunekreef, Bert

AU - Hoek, Gerard

PY - 2013/6/4

Y1 - 2013/6/4

N2 - Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM10 and PM2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed. Good models were developed for Cu, Fe, and Zn in both fractions (PM10 and PM 2.5) explaining on average between 67 and 79% of the concentration variance (R2) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM10 and PM2.5 fractions and Si, Ni, and K in the PM10 fraction performed moderately with R2 ranging from 50 to 61%. Si, NI, and K models for PM2.5 performed poorest with R2 under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE.

AB - Land Use Regression (LUR) models have been used to describe and model spatial variability of annual mean concentrations of traffic related pollutants such as nitrogen dioxide (NO2), nitrogen oxides (NOx) and particulate matter (PM). No models have yet been published of elemental composition. As part of the ESCAPE project, we measured the elemental composition in both the PM10 and PM2.5 fraction sizes at 20 sites in each of 20 study areas across Europe. LUR models for eight a priori selected elements (copper (Cu), iron (Fe), potassium (K), nickel (Ni), sulfur (S), silicon (Si), vanadium (V), and zinc (Zn)) were developed. Good models were developed for Cu, Fe, and Zn in both fractions (PM10 and PM 2.5) explaining on average between 67 and 79% of the concentration variance (R2) with a large variability between areas. Traffic variables were the dominant predictors, reflecting nontailpipe emissions. Models for V and S in the PM10 and PM2.5 fractions and Si, Ni, and K in the PM10 fraction performed moderately with R2 ranging from 50 to 61%. Si, NI, and K models for PM2.5 performed poorest with R2 under 50%. The LUR models are used to estimate exposures to elemental composition in the health studies involved in ESCAPE.

UR - https://www.scopus.com/pages/publications/84878658458

U2 - 10.1021/es400156t

DO - 10.1021/es400156t

M3 - Article

C2 - 23651082

SN - 0013-936X

VL - 47

SP - 5778

EP - 5786

JO - Environmental Science and Technology

JF - Environmental Science and Technology

IS - 11

ER -

Development of land use regression models for particle composition in twenty study areas in Europe

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