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

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  • Hanne Krage Carlsen
  • Erik Bäck, Environment Administration
  • ,
  • Kristina Eneroth, Environment and Health Administration
  • ,
  • Thorarinn Gislason, University of Iceland, Landspitali National University Hospital of Iceland
  • ,
  • Mathias Holm, Sahlgrenska Academy
  • ,
  • Christer Janson, Uppsala universitet
  • ,
  • Steen Solvang Jensen
  • Ane Johannessen, Universitetet i Bergen
  • ,
  • Marko Kaasik, Institute of Physics, Tartu
  • ,
  • Lars Modig, Clinical Sciences, Umea universitet, Klinisk vetenskap.
  • ,
  • David Segersson, Swedish Meteorological and Hydrological Institute
  • ,
  • Torben Sigsgaard
  • Bertil Forsberg, Clinical Sciences, Umea universitet, Klinisk vetenskap.
  • ,
  • David Olsson, Clinical Sciences, Umea universitet, Klinisk vetenskap.
  • ,
  • Hans Orru, Clinical Sciences, Umea universitet, Klinisk vetenskap., University Tartu

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, 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.

TidsskriftAtmospheric Environment
Sider (fra-til)416-425
Antal sider10
StatusUdgivet - 2017

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