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On-Line Monitoring Of Pollution Concentrations With Autoregressive Moving Average Time Series

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  • Christopher Dienes
  • Alexander Aue

Abstract

type="main" xml:id="jtsa12062-abs-0001"> The concentration of aerosol particles, largely caused by traffic volume and often enhanced during temperature inversion episodes in the cold season, can be a concern for human health in the urban environment. This particulate matter is typically recorded as PM 10 , the total mass of particles below 10 μm in diameter. It is suspected that, within the PM 10 class, ultrafine particles ( > 100 nm) may be responsible for causing respiratory and cardiovascular diseases. Because of their low mass, ultrafine particles are hard to detect, and researchers try to utilize PM 10 in combination with nitrogen oxides NO x and other trace gases to monitor their dynamic evolution. To meet pollution standards set by national government and European Union regulation, the city of Klagenfurt, Austria, began using calcium magnesium acetate as a deicer on 11 January 2012, hoping to literally glue pollutants to the ground and thereby reducing pollution concentrations. With the statistical methodology developed in this article, the dynamic evolution of PM 10 and NO x is traced for the time period starting 4 January and ending 25 January 2012, and a change in dynamics is found. The findings are based on on-line monitoring procedures that sequentially detect structural breaks in the mean and the parameter values of an autoregressive moving average process. These are defined in terms of model residuals and one-step ahead predictors. Theoretical properties are studied, and a simulation study shows that the proposed procedures work well in finite samples.

Suggested Citation

  • Christopher Dienes & Alexander Aue, 2014. "On-Line Monitoring Of Pollution Concentrations With Autoregressive Moving Average Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 35(3), pages 239-261, May.
    • Handle: RePEc:bla:jtsera:v:35:y:2014:i:3:p:239-261
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    File URL: http://hdl.handle.net/10.1111/jtsa.12062
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    References listed on IDEAS

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    3. Hoga, Yannick, 2017. "Monitoring multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 105-121.

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