| 1. |
- Carlsen, Hanne Krage, et al.
(author)
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Indicators of residential traffic exposure: Modelled NOX, traffic proximity, and self-reported exposure in RHINE III
- 2017
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In: Atmospheric Environment. - : Elsevier BV. - 1352-2310 .- 1873-2844. ; 167, s. 416-425
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Journal article (peer-reviewed)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, 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. © 2017
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| 2. |
- Cyrys, Josef, et al.
(author)
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Variation of NO2 and NOx concentrations between and within 36 European study areas : Results from the ESCAPE study
- 2012
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In: Atmospheric Environment. - : Elsevier BV. - 1352-2310 .- 1873-2844. ; 62, s. 374-390
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Journal article (peer-reviewed)abstract
- The ESCAPE study (European Study of Cohorts for Air Pollution Effects) investigates long-term effects of exposure to air pollution on human health in Europe. This paper documents the spatial variation of measured NO2 and NOx concentrations between and within 36 ESCAPE study areas across Europe.In all study areas NO2 and NOx were measured using standardized methods between October 2008 and April 2011. On average, 41 sites were selected per study area, including regional and urban background as well as street sites. The measurements were conducted in three different seasons, using Ogawa badges. Average concentrations for each site were calculated after adjustment for temporal variation using data obtained from a routine monitor background site.Substantial spatial variability was found in NO2 and NOx concentrations between and within study areas; 40% of the overall NO2 variance was attributable to the variability between study areas and 60% to variability within study areas. The corresponding values for NOx were 30% and 70%. The within-area spatial variability was mostly determined by differences between street and urban background concentrations. The street/urban background concentration ratio for NO2 varied between 1.09 and 3.16 across areas. The highest median concentrations were observed in Southern Europe, the lowest in Northern Europe.In conclusion, we found significant contrasts in annual average NO2 and NOx concentrations between and especially within 36 study areas across Europe. Epidemiological long-term studies should therefore consider different approaches for better characterization of the intra-urban contrasts, either by increasing of the number of monitors or by modelling.
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| 3. |
- Hazenkamp-Von Arx, M.E., et al.
(author)
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PM2.5 and NO2 assessment in 21 European study centres of ECRHS II : annual means and seasonal differences
- 2004
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In: Atmospheric Environment. - : Elsevier. - 1352-2310 .- 1873-2844. ; 38:13, s. 1943-1953
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Journal article (peer-reviewed)abstract
- The follow-up of cohorts of adults from more than 20 European centres of the former ECRHS I (1989-1992) investigates long-term effects of exposure to ambient air pollution on respiratory health, in particular asthma and change of pulmonary function. Since PM2.5 is not routinely monitored in Europe, we measured PM2.5 concentrations in 21 participating centres to estimate 'background' exposure in these cities. Winter (November-February), summer (May-August) and annual mean (all months) values of PM2.5 were determined from measuring periods between June 2000 and November 2001. Sampling was conducted for 7 days per month for a year. Annual and winter mean concentrations of PM2.5 vary substantially being lowest in Iceland and highest in centres in Northern Italy. Annual mean concentrations ranged from 3.7 to 44.9 mug m(-3), winter mean concentrations from 4.8 to 69.2 mug m(-3), and summer mean concentrations from 3.3 to 23.1 mugm(-3). Seasonal variability occurred but did not follow the same pattern across all centres. Therefore, ranking of centres varied from summer to winter. Simultaneously, NO2 concentrations were measured using passive sampling tubes. Annual mean NO2 concentrations range from 4.9 to 72.1 mug m(-3) with similar seasonal variations across centres and constant ranking of centres between seasons. The correlation between annual NO2 and PM2.5 concentrations is fair (Spearman correlation coefficient r(s) = 0.75), but when considered as monthly means the correlation is far less consistent and varies substantially between centres. The range of PM2.5 mass concentrations obtained in ECRHS II is larger than in other current cohort studies on long-term effects of air pollution. This substantial variation in PM2.5 exposure will improve statistical power in future multilevel health analyses and to some degree may compensate for the lack of information on within-city variability. Seasonal means may be used to indicate potential differences in the toxicity across the year. Across ECRHS cities annual NO2 might serve as a surrogate for PM2.5, especially for past exposure assessment, when PM2.5 is not available.
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| 4. |
- Malmqvist, Ebba, et al.
(author)
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Assessing ozone exposure for epidemiological studies in Malmo and Umea, Sweden
- 2014
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In: Atmospheric Environment. - : Elsevier BV. - 1352-2310 .- 1873-2844. ; 94, s. 241-248
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Journal article (peer-reviewed)abstract
- Ground level ozone [ozone] is considered a harmful air pollutant but there is a knowledge gap regarding its long term health effects. The main aim of this study is to develop local Land Use Regression [LUR] models that can be used to study long term health effects of ozone. The specific aim is to develop spatial LUR models for two Swedish cities, Umea and Malmo, as well as a temporal model for Malmo in order to assess ozone exposure for long term epidemiological studies. For the spatial model we measured ozone, using Ogawa passive samplers, as weekly averages at 40 sites in each study area, during three seasons. This data was then inserted in the LUR-model with data on traffic, land use, population density and altitude to develop explanatory models of ozone variation. To develop the temporal model for Malmo, hourly ozone data was aggregated into daily means for two measurement stations in Malmo and one in a rural area outside Malmo. Using regression analyses we inserted meteorological variables into different temporal models and the one that performed best for all three stations was chosen. For Malmo the LUR-model had an adjusted model R-2 of 0.40 and cross validation R-2 of 0.17. For Umea the model had an adjusted model R-2 of 0.67 and cross validation adjusted R-2 of 0.48. When restricting the model to only including measuring sites from urban areas, the Malmo model had adjusted model R-2 of 0.51 (cross validation adjusted R-2 0.33) and the Umea model had adjusted model R-2 of 0.81 (validation adjusted R-2 of 0.73). The temporal model had adjusted model R-2 0.54 and 0.61 for the two Malmo sites, the cross validation adjusted R-2 was 0.42. In conclusion, we can with moderate accuracy, at least for Umea, predict the spatial variability, and in Malmo the temporal variability in ozone variation. (C) 2014 The Authors. Published by Elsevier Ltd.
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