Modelling long-term averages of local ambient air pollution in Oslo, Norway: evaluation of nitrogen dioxide, PM&lt;SUB align=right&gt;10 and PM&lt;SUB align=right&gt;2.5

Oftedal, Bente; Walker, Sam; Gram, Frederick; McInnes, Harold; Nafstad, Per

doi:10.1504/ijep.2009.021820

Cited by 15 publications

(15 citation statements)

References 19 publications

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“…It can be argued that the EPISODE model gives reasonable estimates of long-term exposure to outdoor air pollution. In the evaluation of the model, the association between the estimates and the measurements on annual level was good, and better than on seasonal or monthly level [34]. However, we do not know the participants total exposure.…”

Section: Discussionmentioning

confidence: 82%

“…We were interested in longterm exposure and received data with yearly average concentrations of NO 2 and particulate matter (PM 10 and PM 2,5 ) from the period 1992-2001, which we collapsed to the average concentration for the entire period. This model has been evaluated and seems to assess long-term residential exposure reasonably well [34]. Compared to measurements from fixed monitoring stations in Oslo, the strongest correlation was found for PM 2,5 and NO 2 .…”

Section: Exposure Assessmentmentioning

confidence: 99%

“…Compared to measurements from fixed monitoring stations in Oslo, the strongest correlation was found for PM 2,5 and NO 2 . The model has been described in more detail in previous papers [1,34].…”

Section: Exposure Assessmentmentioning

confidence: 99%

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Outdoor air pollution, bone density and self-reported forearm fracture: the Oslo Health Study

et al. 2010

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Section: Discussionmentioning

confidence: 82%

Section: Exposure Assessmentmentioning

confidence: 99%

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Outdoor air pollution, bone density and self-reported forearm fracture: the Oslo Health Study

et al. 2010

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“…The air pollution data were estimated using the Air Quality Information System (AirQUIS) developed at the Norwegian Institute of Air Research (NILU) [22]. The estimated values of these pollutants from this model have been evaluated elsewhere [23].…”

Section: Other Study Variablesmentioning

confidence: 99%

Association between tobacco smoke exposure and levels of C-reactive protein in the Oslo II Study

et al. 2007

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It is well known that tobacco smoke exposure is related to the risk of developing cardiovascular diseases and events. One mechanism could be that tobacco smoke acts on the cardiovascular system by altering the autonomic function and/or inducing inflammatory responses. We used data from 3 744 men aged 67-77 years from the city of Oslo that participated in the health screening for the Oslo II Health Study in 2000, to explore associations between C-reactive protein and environmental exposures including exposure to tobacco smoke products. Levels of C-reactive protein were higher in current smokers (2.05 mg/l, IQR, 1.11-4.17 mg/l), compared to former-smokers (1.58 mg/l, IQR, 0.83-3.03 mg/l) and non-smokers (1.26 mg/l, IQR, 0.65-2.40 mg/l). The risk of elevated C-reactive protein increased with both numbers of current cigarettes smoked per day and numbers of pack-years of smoking, when other factors were adjusted for (P < 0.001). We found a positive dose-response relationship between amount of current cigarette smoking and elevated C-reactive protein levels. These findings support the idea that the induction or exacerbation of inflammation could be a mechanism by which smoking promotes atherosclerotic cardiovascular diseases.

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“…It is a mixture of solid and liquid droplets with diameter less than 10 micrometers. See for example Aldrin and Hobaek Haff (2005) and Oftedal et al (2009) for studies on air pollution involving PM10 and other particle pollutants. In their analysis Guo et al (2012) considered two covariates, X 1 (t) the logarithm of the number of cars per hour and the second covariate, X 2 (t), the wind speed (meters/second).…”

Section: Additional Real Data Example: Air Pollution Datamentioning

confidence: 99%

Quantile regression in varying-coefficient models: non-crossing quantile curves and heteroscedasticity

2016

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Quantile regression is an important tool for describing the characteristics of conditional distributions. Population conditional quantile functions cannot cross for different quantile orders. Unfortunately estimated regression quantile curves often violate this and cross each other, which can be very annoying for interpretations and further analysis. In this paper we are concerned with flexible varying-coefficient modelling, and develop methods for quantile regression that ensure that the estimated quantile curves do not cross. A second aim of the paper is to allow for some heteroscedasticity in the error modelling, and to also estimate the associated variability function. We investigate the finite-sample performances of the discussed methods via simulation studies. Some applications to real data illustrate the use of the methods in practical settings.

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Modelling long-term averages of local ambient air pollution in Oslo, Norway: evaluation of nitrogen dioxide, PM<SUB align=right>10 and PM<SUB align=right>2.5

Cited by 15 publications

References 19 publications

Outdoor air pollution, bone density and self-reported forearm fracture: the Oslo Health Study

Outdoor air pollution, bone density and self-reported forearm fracture: the Oslo Health Study

Association between tobacco smoke exposure and levels of C-reactive protein in the Oslo II Study

Quantile regression in varying-coefficient models: non-crossing quantile curves and heteroscedasticity

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