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Torben Sigsgaard

Indicators of residential traffic exposure: Modelled NOX, traffic proximity, and self-reported exposure in RHINE III

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Indicators of residential traffic exposure : Modelled NOX, traffic proximity, and self-reported exposure in RHINE III. / Carlsen, Hanne Krage; Bäck, Erik; Eneroth, Kristina; Gislason, Thorarinn; Holm, Mathias; Janson, Christer; Jensen, Steen Solvang; Johannessen, Ane; Kaasik, Marko; Modig, Lars; Segersson, David; Sigsgaard, Torben; Forsberg, Bertil; Olsson, David; Orru, Hans.

I: Atmospheric Environment, Bind 167, 2017, s. 416-425.

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

Harvard

Carlsen, HK, Bäck, E, Eneroth, K, Gislason, T, Holm, M, Janson, C, Jensen, SS, Johannessen, A, Kaasik, M, Modig, L, Segersson, D, Sigsgaard, T, Forsberg, B, Olsson, D & Orru, H 2017, 'Indicators of residential traffic exposure: Modelled NOX, traffic proximity, and self-reported exposure in RHINE III', Atmospheric Environment, bind 167, s. 416-425. https://doi.org/10.1016/j.atmosenv.2017.08.015

APA

Carlsen, H. K., Bäck, E., Eneroth, K., Gislason, T., Holm, M., Janson, C., ... Orru, H. (2017). Indicators of residential traffic exposure: Modelled NOX, traffic proximity, and self-reported exposure in RHINE III. Atmospheric Environment, 167, 416-425. https://doi.org/10.1016/j.atmosenv.2017.08.015

CBE

Carlsen HK, Bäck E, Eneroth K, Gislason T, Holm M, Janson C, Jensen SS, Johannessen A, Kaasik M, Modig L, Segersson D, Sigsgaard T, Forsberg B, Olsson D, Orru H. 2017. Indicators of residential traffic exposure: Modelled NOX, traffic proximity, and self-reported exposure in RHINE III. Atmospheric Environment. 167:416-425. https://doi.org/10.1016/j.atmosenv.2017.08.015

MLA

Vancouver

Author

Carlsen, Hanne Krage ; Bäck, Erik ; Eneroth, Kristina ; Gislason, Thorarinn ; Holm, Mathias ; Janson, Christer ; Jensen, Steen Solvang ; Johannessen, Ane ; Kaasik, Marko ; Modig, Lars ; Segersson, David ; Sigsgaard, Torben ; Forsberg, Bertil ; Olsson, David ; Orru, Hans. / Indicators of residential traffic exposure : Modelled NOX, traffic proximity, and self-reported exposure in RHINE III. I: Atmospheric Environment. 2017 ; Bind 167. s. 416-425.

Bibtex

@article{a8778569ad004f47961c14a9d697a112,
title = "Indicators of residential traffic exposure: Modelled NOX, traffic proximity, and self-reported exposure in RHINE III",
abstract = "Few studies have investigated associations between self-reported and modelled exposure to traffic pollution. The objective of this study was to examine correlations between self-reported traffic exposure and modelled (a) NOX and (b) traffic proximity in seven different northern European cities; Aarhus (Denmark), Bergen (Norway), Gothenburg, Ume{\aa}, and Uppsala (Sweden), Reykjavik (Iceland), and Tartu (Estonia). We analysed data from the RHINE III (Respiratory Health in Northern Europe, www.rhine.nu) cohorts of the seven study cities. Traffic proximity (distance to the nearest road with >10,000 vehicles per day) was calculated and vehicle exhaust (NOX) was modelled using dispersion models and land-use regression (LUR) data from 2011. Participants were asked a question about self-reported traffic intensity near bedroom window and another about traffic noise exposure at the residence. The data were analysed using rank correlation (Kendall's tau) and inter-rater agreement (Cohen's Kappa) between tertiles of modelled NOX and traffic proximity tertile and traffic proximity categories (0–150 metres (m), 150–200 m, >300 m) in each centre. Data on variables of interest were available for 50–99{\%} of study participants per each cohort. Mean modelled NOX levels were between 6.5 and 16.0 μg/m3; median traffic intensity was between 303 and 10,750 m in each centre. In each centre, 7.7–18.7{\%} of respondents reported exposure to high traffic intensity and 3.6–16.3{\%} of respondents reported high exposure to traffic noise. Self-reported residential traffic exposure had low or no correlation with modelled exposure and traffic proximity in all centres, although results were statistically significant (tau = 0.057–0.305). Self-reported residential traffic noise correlated weakly (tau = 0.090–0.255), with modelled exposure in all centres except Reykjavik. Modelled NOX had the highest correlations between self-reported and modelled traffic exposure in five of seven centres, traffic noise exposure had the highest correlation with traffic proximity in tertiles in three centres. Self-reported exposure to high traffic intensity and traffic noise at each participant's residence had low or weak although statistically significant correlations with modelled vehicle exhaust pollution levels and traffic proximity.",
keywords = "Cohort study, Dispersion models, Land-use regression models, NO, Noise exposure, Traffic exposure",
author = "Carlsen, {Hanne Krage} and Erik B{\"a}ck and Kristina Eneroth and Thorarinn Gislason and Mathias Holm and Christer Janson and Jensen, {Steen Solvang} and Ane Johannessen and Marko Kaasik and Lars Modig and David Segersson and Torben Sigsgaard and Bertil Forsberg and David Olsson and Hans Orru",
year = "2017",
doi = "10.1016/j.atmosenv.2017.08.015",
language = "English",
volume = "167",
pages = "416--425",
journal = "Atmospheric Environment",
issn = "1352-2310",
publisher = "Pergamon Press",

}

RIS

TY - JOUR

T1 - Indicators of residential traffic exposure

T2 - Modelled NOX, traffic proximity, and self-reported exposure in RHINE III

AU - Carlsen, Hanne Krage

AU - Bäck, Erik

AU - Eneroth, Kristina

AU - Gislason, Thorarinn

AU - Holm, Mathias

AU - Janson, Christer

AU - Jensen, Steen Solvang

AU - Johannessen, Ane

AU - Kaasik, Marko

AU - Modig, Lars

AU - Segersson, David

AU - Sigsgaard, Torben

AU - Forsberg, Bertil

AU - Olsson, David

AU - Orru, Hans

PY - 2017

Y1 - 2017

N2 - Few studies have investigated associations between self-reported and modelled exposure to traffic pollution. The objective of this study was to examine correlations between self-reported traffic exposure and modelled (a) NOX and (b) traffic proximity in seven different northern European cities; Aarhus (Denmark), Bergen (Norway), Gothenburg, Umeå, and Uppsala (Sweden), Reykjavik (Iceland), and Tartu (Estonia). We analysed data from the RHINE III (Respiratory Health in Northern Europe, www.rhine.nu) cohorts of the seven study cities. Traffic proximity (distance to the nearest road with >10,000 vehicles per day) was calculated and vehicle exhaust (NOX) was modelled using dispersion models and land-use regression (LUR) data from 2011. Participants were asked a question about self-reported traffic intensity near bedroom window and another about traffic noise exposure at the residence. The data were analysed using rank correlation (Kendall's tau) and inter-rater agreement (Cohen's Kappa) between tertiles of modelled NOX and traffic proximity tertile and traffic proximity categories (0–150 metres (m), 150–200 m, >300 m) in each centre. Data on variables of interest were available for 50–99% of study participants per each cohort. Mean modelled NOX levels were between 6.5 and 16.0 μg/m3; median traffic intensity was between 303 and 10,750 m in each centre. In each centre, 7.7–18.7% of respondents reported exposure to high traffic intensity and 3.6–16.3% of respondents reported high exposure to traffic noise. Self-reported residential traffic exposure had low or no correlation with modelled exposure and traffic proximity in all centres, although results were statistically significant (tau = 0.057–0.305). Self-reported residential traffic noise correlated weakly (tau = 0.090–0.255), with modelled exposure in all centres except Reykjavik. Modelled NOX had the highest correlations between self-reported and modelled traffic exposure in five of seven centres, traffic noise exposure had the highest correlation with traffic proximity in tertiles in three centres. Self-reported exposure to high traffic intensity and traffic noise at each participant's residence had low or weak although statistically significant correlations with modelled vehicle exhaust pollution levels and traffic proximity.

AB - Few studies have investigated associations between self-reported and modelled exposure to traffic pollution. The objective of this study was to examine correlations between self-reported traffic exposure and modelled (a) NOX and (b) traffic proximity in seven different northern European cities; Aarhus (Denmark), Bergen (Norway), Gothenburg, Umeå, and Uppsala (Sweden), Reykjavik (Iceland), and Tartu (Estonia). We analysed data from the RHINE III (Respiratory Health in Northern Europe, www.rhine.nu) cohorts of the seven study cities. Traffic proximity (distance to the nearest road with >10,000 vehicles per day) was calculated and vehicle exhaust (NOX) was modelled using dispersion models and land-use regression (LUR) data from 2011. Participants were asked a question about self-reported traffic intensity near bedroom window and another about traffic noise exposure at the residence. The data were analysed using rank correlation (Kendall's tau) and inter-rater agreement (Cohen's Kappa) between tertiles of modelled NOX and traffic proximity tertile and traffic proximity categories (0–150 metres (m), 150–200 m, >300 m) in each centre. Data on variables of interest were available for 50–99% of study participants per each cohort. Mean modelled NOX levels were between 6.5 and 16.0 μg/m3; median traffic intensity was between 303 and 10,750 m in each centre. In each centre, 7.7–18.7% of respondents reported exposure to high traffic intensity and 3.6–16.3% of respondents reported high exposure to traffic noise. Self-reported residential traffic exposure had low or no correlation with modelled exposure and traffic proximity in all centres, although results were statistically significant (tau = 0.057–0.305). Self-reported residential traffic noise correlated weakly (tau = 0.090–0.255), with modelled exposure in all centres except Reykjavik. Modelled NOX had the highest correlations between self-reported and modelled traffic exposure in five of seven centres, traffic noise exposure had the highest correlation with traffic proximity in tertiles in three centres. Self-reported exposure to high traffic intensity and traffic noise at each participant's residence had low or weak although statistically significant correlations with modelled vehicle exhaust pollution levels and traffic proximity.

KW - Cohort study

KW - Dispersion models

KW - Land-use regression models

KW - NO

KW - Noise exposure

KW - Traffic exposure

UR - http://www.scopus.com/inward/record.url?scp=85028350198&partnerID=8YFLogxK

U2 - 10.1016/j.atmosenv.2017.08.015

DO - 10.1016/j.atmosenv.2017.08.015

M3 - Journal article

VL - 167

SP - 416

EP - 425

JO - Atmospheric Environment

JF - Atmospheric Environment

SN - 1352-2310

ER -