Impact and uncertainty of a traffic management intervention: Population exposure to polycyclic aromatic hydrocarbons

Sotiris Vardoulakis*, Zaid Chalabi, Tony Fletcher, Chris Grundy, Giovanni S. Leonardi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


In urban areas, road traffic is a major source of carcinogenic polycyclic aromatic hydrocarbons (PAH), thus any changes in traffic patterns are expected to affect PAH concentrations in ambient air. Exposure to PAH and other traffic-related air pollutants has often been quantified in a deterministic manner that disregards the various sources of uncertainty in the modelling systems used. In this study, we developed a generic method for handling uncertainty in population exposure models. The method was applied to quantify the uncertainty in population exposure to benzo[a]pyrene (BaP) before and after the implementation of a traffic management intervention. This intervention would affect the movement of vehicles in the studied area and consequently alter traffic emissions, pollutant concentrations and population exposure. Several models, including an emission calculator, a dispersion model and a Geographic Information System were used to quantify the impact of the traffic management intervention. We established four exposure zones defined by distance of residence postcode centroids from major road or intersection. A stochastic method was used to quantify the uncertainty in the population exposure model. The method characterises uncertainty using probability measures and propagates it applying Monte Carlo analysis. The overall model predicted that the traffic management scheme would lead to a minor reduction in mean population exposure to BaP in the studied area. However, the uncertainty associated with the exposure estimates was much larger than this reduction. The proposed method is generic and provides realistic estimates of population exposure to traffic-related pollutants, as well as characterises the uncertainty in these estimates. This method can be used within a decision support tool to evaluate the impact of alternative traffic management policies.

Original languageEnglish
Pages (from-to)244-251
Number of pages8
JournalScience of the Total Environment, The
Issue number2-3
Publication statusPublished - 15 May 2008

Bibliographical note

Funding Information:
This study was carried out as part of the Pollutants in the Urban Environment (PUrE) project funded by EPSRC Sustainable Urban Environment Programme. We are grateful to our partners in PUrE for their support; Dr. Ben Armstrong (LSHTM) for comments on the manuscript; Birmingham City Council, National Air Quality Information Archive, and British Atmospheric Data Centre for the datasets provided. The census data are Crown copyright reproduced with the permission of HMSO. Any views or opinions presented in this paper are those of the authors and do not represent the views of the United Kingdom Health Protection Agency.


  • Dispersion model
  • Environmental exposure
  • PAH
  • Parametric uncertainty
  • Spatial variability
  • Urban air pollution


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