Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies

Kees de Hoogh; Michal Korek; Danielle Vienneau; Menno Keuken; Jaakko Kukkonen; Mark J Nieuwenhuijsen; Chiara Badaloni; Rob Beelen; Andrea Bolignano; Giulia Cesaroni; Marta Cirach Pradas; Josef Cyrys; John Douros; Marloes Eeftens; Francesco Forastiere; Bertil Forsberg; Kateryna Fuks; Ulrike Gehring; Alexandros Gryparis; John Gulliver; Anna L Hansell; Barbara Hoffmann; Christer Johansson; Sander Jonkers; Leena Kangas; Klea Katsouyanni; Nino Künzli; Timo Lanki; Michael Memmesheimer; Nicolas Moussiopoulos; Lars Modig; Göran Pershagen; Nicole Probst-Hensch; Christian Schindler; Tamara Schikowski; Dorothee Sugiri; Oriol Teixidó; Ming-Yi Tsai; Tarja Yli-Tuomi; Bert Brunekreef; Gerard Hoek; Tom Bellander

doi:10.1016/j.envint.2014.08.011

Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies

Kees de Hoogh, Michal Korek, Danielle Vienneau, Menno Keuken, Jaakko Kukkonen, Mark J Nieuwenhuijsen, Chiara Badaloni, Rob Beelen, Andrea Bolignano, Giulia Cesaroni, Marta Cirach Pradas, Josef Cyrys, John Douros, Marloes Eeftens, Francesco Forastiere, Bertil Forsberg, Kateryna Fuks, Ulrike Gehring, Alexandros Gryparis, John GulliverAnna L Hansell, Barbara Hoffmann, Christer Johansson, Sander Jonkers, Leena Kangas, Klea Katsouyanni, Nino Künzli, Timo Lanki, Michael Memmesheimer, Nicolas Moussiopoulos, Lars Modig, Göran Pershagen, Nicole Probst-Hensch, Christian Schindler, Tamara Schikowski, Dorothee Sugiri, Oriol Teixidó, Ming-Yi Tsai, Tarja Yli-Tuomi, Bert Brunekreef, Gerard Hoek, Tom Bellander

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

Abstract

BACKGROUND: Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. Few comparisons have however been made of the performance of these methods.

OBJECTIVES: Within the European Study of Cohorts for Air Pollution Effects (ESCAPE) we explored the differences between LUR and DM estimates for NO2, PM10 and PM2.5.

METHODS: The ESCAPE study developed LUR models for outdoor air pollution levels based on a harmonised monitoring campaign. In thirteen ESCAPE study areas we further applied dispersion models. We compared LUR and DM estimates at the residential addresses of participants in 13 cohorts for NO2; 7 for PM10 and 4 for PM2.5. Additionally, we compared the DM estimates with measured concentrations at the 20-40 ESCAPE monitoring sites in each area.

RESULTS: The median Pearson R (range) correlation coefficients between LUR and DM estimates for the annual average concentrations of NO2, PM10 and PM2.5 were 0.75 (0.19-0.89), 0.39 (0.23-0.66) and 0.29 (0.22-0.81) for 112,971 (13 study areas), 69,591 (7) and 28,519 (4) addresses respectively. The median Pearson R correlation coefficients (range) between DM estimates and ESCAPE measurements were of 0.74 (0.09-0.86) for NO2; 0.58 (0.36-0.88) for PM10 and 0.58 (0.39-0.66) for PM2.5.

CONCLUSIONS: LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5.

Original language	English
Pages (from-to)	382-92
Number of pages	11
Journal	Environment international
Volume	73
DOIs	https://doi.org/10.1016/j.envint.2014.08.011
Publication status	Published - Dec 2014

Bibliographical note

Keywords

Land use regression
Dispersion modelling
Air pollution
Exposure
Cohort

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de Hoogh, K., Korek, M., Vienneau, D., Keuken, M., Kukkonen, J., Nieuwenhuijsen, M. J., Badaloni, C., Beelen, R., Bolignano, A., Cesaroni, G., Pradas, M. C., Cyrys, J., Douros, J., Eeftens, M., Forastiere, F., Forsberg, B., Fuks, K., Gehring, U., Gryparis, A., ... Bellander, T. (2014). Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies. Environment international, 73, 382-92. https://doi.org/10.1016/j.envint.2014.08.011

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title = "Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies",

abstract = "BACKGROUND: Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. Few comparisons have however been made of the performance of these methods.OBJECTIVES: Within the European Study of Cohorts for Air Pollution Effects (ESCAPE) we explored the differences between LUR and DM estimates for NO2, PM10 and PM2.5.METHODS: The ESCAPE study developed LUR models for outdoor air pollution levels based on a harmonised monitoring campaign. In thirteen ESCAPE study areas we further applied dispersion models. We compared LUR and DM estimates at the residential addresses of participants in 13 cohorts for NO2; 7 for PM10 and 4 for PM2.5. Additionally, we compared the DM estimates with measured concentrations at the 20-40 ESCAPE monitoring sites in each area.RESULTS: The median Pearson R (range) correlation coefficients between LUR and DM estimates for the annual average concentrations of NO2, PM10 and PM2.5 were 0.75 (0.19-0.89), 0.39 (0.23-0.66) and 0.29 (0.22-0.81) for 112,971 (13 study areas), 69,591 (7) and 28,519 (4) addresses respectively. The median Pearson R correlation coefficients (range) between DM estimates and ESCAPE measurements were of 0.74 (0.09-0.86) for NO2; 0.58 (0.36-0.88) for PM10 and 0.58 (0.39-0.66) for PM2.5.CONCLUSIONS: LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5.",

keywords = "Land use regression, Dispersion modelling, Air pollution, Exposure, Cohort",

author = "\{de Hoogh\}, Kees and Michal Korek and Danielle Vienneau and Menno Keuken and Jaakko Kukkonen and Nieuwenhuijsen, \{Mark J\} and Chiara Badaloni and Rob Beelen and Andrea Bolignano and Giulia Cesaroni and Pradas, \{Marta Cirach\} and Josef Cyrys and John Douros and Marloes Eeftens and Francesco Forastiere and Bertil Forsberg and Kateryna Fuks and Ulrike Gehring and Alexandros Gryparis and John Gulliver and Hansell, \{Anna L\} and Barbara Hoffmann and Christer Johansson and Sander Jonkers and Leena Kangas and Klea Katsouyanni and Nino K{\"u}nzli and Timo Lanki and Michael Memmesheimer and Nicolas Moussiopoulos and Lars Modig and G{\"o}ran Pershagen and Nicole Probst-Hensch and Christian Schindler and Tamara Schikowski and Dorothee Sugiri and Oriol Teixid{\'o} and Ming-Yi Tsai and Tarja Yli-Tuomi and Bert Brunekreef and Gerard Hoek and Tom Bellander",

year = "2014",

month = dec,

doi = "10.1016/j.envint.2014.08.011",

language = "English",

volume = "73",

pages = "382--92",

journal = "Environment international",

issn = "0160-4120",

publisher = "Elsevier Limited",

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de Hoogh, K, Korek, M, Vienneau, D, Keuken, M, Kukkonen, J, Nieuwenhuijsen, MJ, Badaloni, C, Beelen, R, Bolignano, A, Cesaroni, G, Pradas, MC, Cyrys, J, Douros, J, Eeftens, M, Forastiere, F, Forsberg, B, Fuks, K, Gehring, U, Gryparis, A, Gulliver, J, Hansell, AL, Hoffmann, B, Johansson, C, Jonkers, S, Kangas, L, Katsouyanni, K, Künzli, N, Lanki, T, Memmesheimer, M, Moussiopoulos, N, Modig, L, Pershagen, G, Probst-Hensch, N, Schindler, C, Schikowski, T, Sugiri, D, Teixidó, O, Tsai, M-Y, Yli-Tuomi, T, Brunekreef, B , Hoek, G & Bellander, T 2014, 'Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies', Environment international, vol. 73, pp. 382-92. https://doi.org/10.1016/j.envint.2014.08.011

TY - JOUR

T1 - Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies

AU - de Hoogh, Kees

AU - Korek, Michal

AU - Vienneau, Danielle

AU - Keuken, Menno

AU - Kukkonen, Jaakko

AU - Nieuwenhuijsen, Mark J

AU - Badaloni, Chiara

AU - Beelen, Rob

AU - Bolignano, Andrea

AU - Cesaroni, Giulia

AU - Pradas, Marta Cirach

AU - Cyrys, Josef

AU - Douros, John

AU - Eeftens, Marloes

AU - Forastiere, Francesco

AU - Forsberg, Bertil

AU - Fuks, Kateryna

AU - Gehring, Ulrike

AU - Gryparis, Alexandros

AU - Gulliver, John

AU - Hansell, Anna L

AU - Hoffmann, Barbara

AU - Johansson, Christer

AU - Jonkers, Sander

AU - Kangas, Leena

AU - Katsouyanni, Klea

AU - Künzli, Nino

AU - Lanki, Timo

AU - Memmesheimer, Michael

AU - Moussiopoulos, Nicolas

AU - Modig, Lars

AU - Pershagen, Göran

AU - Probst-Hensch, Nicole

AU - Schindler, Christian

AU - Schikowski, Tamara

AU - Sugiri, Dorothee

AU - Teixidó, Oriol

AU - Tsai, Ming-Yi

AU - Yli-Tuomi, Tarja

AU - Brunekreef, Bert

AU - Hoek, Gerard

AU - Bellander, Tom

PY - 2014/12

Y1 - 2014/12

N2 - BACKGROUND: Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. Few comparisons have however been made of the performance of these methods.OBJECTIVES: Within the European Study of Cohorts for Air Pollution Effects (ESCAPE) we explored the differences between LUR and DM estimates for NO2, PM10 and PM2.5.METHODS: The ESCAPE study developed LUR models for outdoor air pollution levels based on a harmonised monitoring campaign. In thirteen ESCAPE study areas we further applied dispersion models. We compared LUR and DM estimates at the residential addresses of participants in 13 cohorts for NO2; 7 for PM10 and 4 for PM2.5. Additionally, we compared the DM estimates with measured concentrations at the 20-40 ESCAPE monitoring sites in each area.RESULTS: The median Pearson R (range) correlation coefficients between LUR and DM estimates for the annual average concentrations of NO2, PM10 and PM2.5 were 0.75 (0.19-0.89), 0.39 (0.23-0.66) and 0.29 (0.22-0.81) for 112,971 (13 study areas), 69,591 (7) and 28,519 (4) addresses respectively. The median Pearson R correlation coefficients (range) between DM estimates and ESCAPE measurements were of 0.74 (0.09-0.86) for NO2; 0.58 (0.36-0.88) for PM10 and 0.58 (0.39-0.66) for PM2.5.CONCLUSIONS: LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5.

AB - BACKGROUND: Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. Few comparisons have however been made of the performance of these methods.OBJECTIVES: Within the European Study of Cohorts for Air Pollution Effects (ESCAPE) we explored the differences between LUR and DM estimates for NO2, PM10 and PM2.5.METHODS: The ESCAPE study developed LUR models for outdoor air pollution levels based on a harmonised monitoring campaign. In thirteen ESCAPE study areas we further applied dispersion models. We compared LUR and DM estimates at the residential addresses of participants in 13 cohorts for NO2; 7 for PM10 and 4 for PM2.5. Additionally, we compared the DM estimates with measured concentrations at the 20-40 ESCAPE monitoring sites in each area.RESULTS: The median Pearson R (range) correlation coefficients between LUR and DM estimates for the annual average concentrations of NO2, PM10 and PM2.5 were 0.75 (0.19-0.89), 0.39 (0.23-0.66) and 0.29 (0.22-0.81) for 112,971 (13 study areas), 69,591 (7) and 28,519 (4) addresses respectively. The median Pearson R correlation coefficients (range) between DM estimates and ESCAPE measurements were of 0.74 (0.09-0.86) for NO2; 0.58 (0.36-0.88) for PM10 and 0.58 (0.39-0.66) for PM2.5.CONCLUSIONS: LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5.

KW - Land use regression

KW - Dispersion modelling

KW - Air pollution

KW - Exposure

KW - Cohort

U2 - 10.1016/j.envint.2014.08.011

DO - 10.1016/j.envint.2014.08.011

M3 - Article

C2 - 25233102

SN - 0160-4120

VL - 73

SP - 382

EP - 392

JO - Environment international

JF - Environment international

ER -

Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies

Abstract

Bibliographical note

Keywords

UN SDGs

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