Investigation into the use of the CUSUM technique in identifying changes in mean air pollution levels following introduction of a traffic management scheme

Benjamin Barratt; Richard Atkinson; H Ross Anderson; Sean Beevers; Frank Kelly; Ian Mudway; Paul Wilkinson ORCID logo; (2007) Investigation into the use of the CUSUM technique in identifying changes in mean air pollution levels following introduction of a traffic management scheme. Atmospheric environment (Oxford, England, 41 (8). pp. 1784-1791. ISSN 1352-2310 DOI: 10.1016/j.atmosenv.2006.09.052
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There is an increasing need for statistical techniques to identify and quantify the effects of traffic management schemes on ambient pollution levels. Cumulative sum (CUSUM) charts have been used extensively in industrial process control to detect deviations in production parameters from pre-determined values. This study investigates the use of the CUSUM procedure to identify change in ambient air pollution levels following the introduction of a traffic management scheme at a specific location in Central London. The CUSUM methods of Lucas first compute the standardised deviations of time series observations from the desired process mean. These are accumulated over time to compute the CUSUM at each time point. Data for the analysis were taken from a kerbside monitoring site on Marylebone Road, a six lane trunk route in Central London. In August 2001 the lane adjacent to the monitoring site was designated as a permanent bus lane. The CUSUM analysis clearly identifies a sustained decrease in carbon monoxide concentrations beginning in 2002. However, seasonality and other factors precluded precise characterisation of the timing of the change. When the analysis was repeated using a reference mean that extrapolated the pre-intervention trend in carbon monoxide concentrations, the CUSUM chart no longer identified a sustained decrease. CUSUM appears to offer a simple and rapid method for identifying sustained changes in pollution levels, but the range of confounding influences on carbon monoxide concentrations, most notably underlying trends, seasonality and independent interventions, complicate its interpretation. Its application in assessing the presence or timing of a stepped change in pollution or similar environmental time series data is recommended in its basic form only where the predicted change is large by comparison with other independent influences. The authors believe that further development of the technique beyond this initial study is worthwhile in order to improve the technique's sensitivity. © 2006 Elsevier Ltd. All rights reserved.

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