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

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Title: Indicators of residential traffic exposure: Modelled NOX, traffic proximity, and self-reported exposure in RHINE III.
Authors: Carlsen, Hanne Krage1,2,3 hannekcarlsen@envmed.umu.se, Bäck, Erik4, Eneroth, Kristina5, Gislason, Thorarinn6,7, Holm, Mathias3, Janson, Christer8, Jensen, Steen Solvang9, Johannessen, Ane10, Kaasik, Marko11, Modig, Lars12, Segersson, David13, Sigsgaard, Torben14, Forsberg, Bertil1, Olsson, David1, Orru, Hans1,15
Source: Atmospheric Environment. Oct2017, Vol. 167, p416-425. 10p.
Subject Terms: *Environmental indicators, *Land use, Traffic engineering & the environment, Residential areas, Dispersion (Atmospheric chemistry), Rank correlation (Statistics)
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) NO X 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 (NO X ) 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 NO X 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 NO X levels were between 6.5 and 16.0 μg/m 3 ; 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 NO X 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. [ABSTRACT FROM AUTHOR]
Copyright of Atmospheric Environment is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Indicators of residential traffic exposure: Modelled NOX, traffic proximity, and self-reported exposure in RHINE III.
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  Data: <searchLink fieldCode="AR" term="%22Carlsen%2C+Hanne+Krage%22">Carlsen, Hanne Krage</searchLink><relatesTo>1,2,3</relatesTo><i> hannekcarlsen@envmed.umu.se</i><br /><searchLink fieldCode="AR" term="%22Bäck%2C+Erik%22">Bäck, Erik</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Eneroth%2C+Kristina%22">Eneroth, Kristina</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Gislason%2C+Thorarinn%22">Gislason, Thorarinn</searchLink><relatesTo>6,7</relatesTo><br /><searchLink fieldCode="AR" term="%22Holm%2C+Mathias%22">Holm, Mathias</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Janson%2C+Christer%22">Janson, Christer</searchLink><relatesTo>8</relatesTo><br /><searchLink fieldCode="AR" term="%22Jensen%2C+Steen+Solvang%22">Jensen, Steen Solvang</searchLink><relatesTo>9</relatesTo><br /><searchLink fieldCode="AR" term="%22Johannessen%2C+Ane%22">Johannessen, Ane</searchLink><relatesTo>10</relatesTo><br /><searchLink fieldCode="AR" term="%22Kaasik%2C+Marko%22">Kaasik, Marko</searchLink><relatesTo>11</relatesTo><br /><searchLink fieldCode="AR" term="%22Modig%2C+Lars%22">Modig, Lars</searchLink><relatesTo>12</relatesTo><br /><searchLink fieldCode="AR" term="%22Segersson%2C+David%22">Segersson, David</searchLink><relatesTo>13</relatesTo><br /><searchLink fieldCode="AR" term="%22Sigsgaard%2C+Torben%22">Sigsgaard, Torben</searchLink><relatesTo>14</relatesTo><br /><searchLink fieldCode="AR" term="%22Forsberg%2C+Bertil%22">Forsberg, Bertil</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Olsson%2C+David%22">Olsson, David</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Orru%2C+Hans%22">Orru, Hans</searchLink><relatesTo>1,15</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Atmospheric+Environment%22">Atmospheric Environment</searchLink>. Oct2017, Vol. 167, p416-425. 10p.
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  Data: 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) NO X 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 (NO X ) 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 NO X 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 NO X levels were between 6.5 and 16.0 μg/m 3 ; 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 NO X 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. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Atmospheric Environment is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.atmosenv.2017.08.015
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        Text: English
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      – SubjectFull: Land use
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      – SubjectFull: Traffic engineering & the environment
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      – SubjectFull: Dispersion (Atmospheric chemistry)
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      – SubjectFull: Rank correlation (Statistics)
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      – TitleFull: Indicators of residential traffic exposure: Modelled NOX, traffic proximity, and self-reported exposure in RHINE III.
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              Text: Oct2017
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