A principal components analysis of the factors effecting personal exposure to air pollution in urban commuters in Dublin, Ireland

Abstract

Principal component analysis was used to examine air pollution personal exposure data of four urban commuter transport modes for their interrelationships between pollutants and relationships with traffic and meteorological data. Air quality samples of PM_2.5 and VOCs were recorded during peak traffic congestion for the car, bus, cyclist and pedestrian between January 2005 and June 2006 on a busy route in Dublin, Ireland. In total, 200 personal exposure samples were recorded each comprising 17 variables describing the personal exposure concentrations, meteorological conditions and traffic conditions. The data reduction technique, principal component analysis (PCA), was used to create weighted linear combinations of the data and these were subsequently examined for interrelationships between the many variables recorded. The results of the PCA found that personal exposure concentrations in non-motorised forms of transport were influenced to a higher degree by wind speed, whereas personal exposure concentrations in motorised forms of transport were influenced to a higher degree by traffic congestion. The findings of the investigation show that the most effective mechanisms of personal exposure reduction differ between motorised and non-motorised modes of commuter transport.

Keywords:

• Personal exposure
• data reduction
• principal component analysis
• commuter transport

Acknowledgments

The authors would like to thank the Centre for Transport Research and the ETI project (ERTDI programme), Trinity College Dublin, for providing the funding to carry out this research.