A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Indicators of residential traffic exposure: Modelled NOX, traffic proximity, and self-reported exposure in RHINE III
https://orcid.org/0000-0003-1656-9624
https://orcid.org/0000-0001-5923-8441
https://orcid.org/0000-0002-2082-4272
https://orcid.org/0000-0002-5935-962X
AbstractFew 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.
HighlightsThe association between self-reported and modelled traffic exposure was studied in low-pollution Northern European cities.Self-reported exposure measures were residential traffic exposure and traffic noise exposure.Modelled exposure measures were NOX dispersion and proximity to large roads.Self-reported traffic exposure and NOX exposure and traffic proximity had no or weak correlations.Traffic noise exposure had higher agreement with modelled exposure than self-reported traffic exposure in most locations.
Indicators of residential traffic exposure: Modelled NOX, traffic proximity, and self-reported exposure in RHINE III
https://orcid.org/0000-0003-1656-9624
https://orcid.org/0000-0001-5923-8441
https://orcid.org/0000-0002-2082-4272
https://orcid.org/0000-0002-5935-962X
AbstractFew 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.
HighlightsThe association between self-reported and modelled traffic exposure was studied in low-pollution Northern European cities.Self-reported exposure measures were residential traffic exposure and traffic noise exposure.Modelled exposure measures were NOX dispersion and proximity to large roads.Self-reported traffic exposure and NOX exposure and traffic proximity had no or weak correlations.Traffic noise exposure had higher agreement with modelled exposure than self-reported traffic exposure in most locations.
Indicators of residential traffic exposure: Modelled NOX, traffic proximity, and self-reported exposure in RHINE III
https://orcid.org/0000-0003-1656-9624
https://orcid.org/0000-0001-5923-8441
https://orcid.org/0000-0002-2082-4272
https://orcid.org/0000-0002-5935-962X
AbstractFew 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.
HighlightsThe association between self-reported and modelled traffic exposure was studied in low-pollution Northern European cities.Self-reported exposure measures were residential traffic exposure and traffic noise exposure.Modelled exposure measures were NOX dispersion and proximity to large roads.Self-reported traffic exposure and NOX exposure and traffic proximity had no or weak correlations.Traffic noise exposure had higher agreement with modelled exposure than self-reported traffic exposure in most locations.
Indicators of residential traffic exposure: Modelled NOX, traffic proximity, and self-reported exposure in RHINE III
Carlsen, Hanne Krage (author) / Bäck, Erik (author) / Eneroth, Kristina (author) / Gislason, Thorarinn (author) / Holm, Mathias (author) / Janson, Christer (author) / Jensen, Steen Solvang (author) / Johannessen, Ane (author) / Kaasik, Marko (author) / Modig, Lars (author)
Atmospheric Environment ; 167 ; 416-425
2017-08-06
10 pages
Article (Journal)
Electronic Resource
English
Residential proximity to traffic and female pubertal development
| Online Contents | 2016
Residential proximity to traffic and female pubertal development
| Elsevier | 2016
Residential proximity to traffic and female pubertal development
| Online Contents | 2016
| British Library Online Contents | 2014