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From the same journal

Detecting and characterising small changes in urban nitrogen dioxide concentrations

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Detecting and characterising small changes in urban nitrogen dioxide concentrations. / Carslaw, David C.; Carslaw, Nicola.

In: Atmospheric Environment, Vol. 41, No. 22, 07.2007, p. 4723-4733.

Research output: Contribution to journalArticle

Harvard

Carslaw, DC & Carslaw, N 2007, 'Detecting and characterising small changes in urban nitrogen dioxide concentrations', Atmospheric Environment, vol. 41, no. 22, pp. 4723-4733. https://doi.org/10.1016/j.atmosenv.2007.03.034

APA

Carslaw, D. C., & Carslaw, N. (2007). Detecting and characterising small changes in urban nitrogen dioxide concentrations. Atmospheric Environment, 41(22), 4723-4733. https://doi.org/10.1016/j.atmosenv.2007.03.034

Vancouver

Carslaw DC, Carslaw N. Detecting and characterising small changes in urban nitrogen dioxide concentrations. Atmospheric Environment. 2007 Jul;41(22):4723-4733. https://doi.org/10.1016/j.atmosenv.2007.03.034

Author

Carslaw, David C. ; Carslaw, Nicola. / Detecting and characterising small changes in urban nitrogen dioxide concentrations. In: Atmospheric Environment. 2007 ; Vol. 41, No. 22. pp. 4723-4733.

Bibtex - Download

@article{5342a7e823d24dc4839b9474c1ebfb61,
title = "Detecting and characterising small changes in urban nitrogen dioxide concentrations",
abstract = "Detecting and quantifying abnormal changes in the concentration of urban air pollutants can be difficult due to the influence of meteorology and atmospheric chemistry. This study presents methods to detect and characterise small changes in the concentration of nitrogen dioxide (NO2) that deviate from expected behaviour. Generalized additive models (GAMs) are used to describe daily mean NO2 concentrations at roadside monitoring sites to determine how concentrations deviate from measured concentrations. Structural change methods are applied to time series describing this difference to identify change-points, where concentrations of NO2 deviate significantly from expected behaviour. Methods are also used which quantify the timing and uncertainty associated in the timing of these change-points. For most time series data considered in London, concentrations of NO2 underwent relatively abrupt changes rather than smoothly varying increases; changes which remain largely undetected in raw time series data. Most change-points occurred in late 2002 or early 2003, and the factors, which may have contributed to them, are discussed. These methods can also identify technical problems with monitoring equipment and are applicable to other instances where detecting sudden atmospheric composition change is important. {\circledC} 2007 Elsevier Ltd. All rights reserved.",
keywords = "Change-point, Meteorological normalisation, Primary nitrogen dioxide, Structural change, Trend",
author = "Carslaw, {David C.} and Nicola Carslaw",
note = "Cited By :20 Export Date: 19 July 2016",
year = "2007",
month = "7",
doi = "10.1016/j.atmosenv.2007.03.034",
language = "English",
volume = "41",
pages = "4723--4733",
journal = "Atmospheric Environment",
issn = "1352-2310",
publisher = "Elsevier Limited",
number = "22",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Detecting and characterising small changes in urban nitrogen dioxide concentrations

AU - Carslaw, David C.

AU - Carslaw, Nicola

N1 - Cited By :20 Export Date: 19 July 2016

PY - 2007/7

Y1 - 2007/7

N2 - Detecting and quantifying abnormal changes in the concentration of urban air pollutants can be difficult due to the influence of meteorology and atmospheric chemistry. This study presents methods to detect and characterise small changes in the concentration of nitrogen dioxide (NO2) that deviate from expected behaviour. Generalized additive models (GAMs) are used to describe daily mean NO2 concentrations at roadside monitoring sites to determine how concentrations deviate from measured concentrations. Structural change methods are applied to time series describing this difference to identify change-points, where concentrations of NO2 deviate significantly from expected behaviour. Methods are also used which quantify the timing and uncertainty associated in the timing of these change-points. For most time series data considered in London, concentrations of NO2 underwent relatively abrupt changes rather than smoothly varying increases; changes which remain largely undetected in raw time series data. Most change-points occurred in late 2002 or early 2003, and the factors, which may have contributed to them, are discussed. These methods can also identify technical problems with monitoring equipment and are applicable to other instances where detecting sudden atmospheric composition change is important. © 2007 Elsevier Ltd. All rights reserved.

AB - Detecting and quantifying abnormal changes in the concentration of urban air pollutants can be difficult due to the influence of meteorology and atmospheric chemistry. This study presents methods to detect and characterise small changes in the concentration of nitrogen dioxide (NO2) that deviate from expected behaviour. Generalized additive models (GAMs) are used to describe daily mean NO2 concentrations at roadside monitoring sites to determine how concentrations deviate from measured concentrations. Structural change methods are applied to time series describing this difference to identify change-points, where concentrations of NO2 deviate significantly from expected behaviour. Methods are also used which quantify the timing and uncertainty associated in the timing of these change-points. For most time series data considered in London, concentrations of NO2 underwent relatively abrupt changes rather than smoothly varying increases; changes which remain largely undetected in raw time series data. Most change-points occurred in late 2002 or early 2003, and the factors, which may have contributed to them, are discussed. These methods can also identify technical problems with monitoring equipment and are applicable to other instances where detecting sudden atmospheric composition change is important. © 2007 Elsevier Ltd. All rights reserved.

KW - Change-point

KW - Meteorological normalisation

KW - Primary nitrogen dioxide

KW - Structural change

KW - Trend

UR - http://www.scopus.com/inward/record.url?scp=34249337698&partnerID=8YFLogxK

U2 - 10.1016/j.atmosenv.2007.03.034

DO - 10.1016/j.atmosenv.2007.03.034

M3 - Article

VL - 41

SP - 4723

EP - 4733

JO - Atmospheric Environment

JF - Atmospheric Environment

SN - 1352-2310

IS - 22

ER -