Exposure to particulate matter in traffic: A comparison of cyclists and car passengers
Introduction
Adverse health effects of exposure to air pollution have traditionally and consistently been associated with ambient measurements at fixed monitoring stations in many different countries (Künzli et al., 2000, Nawrot et al., 2007, Pope et al., 2009). The exact contribution of different compounds and fractions of particulate matter (PM) to specific health endpoints has not been fully elicited but emissions of internal combustion engines and traffic have been suggested to be more toxic than the general mixture (Jerrett et al., 2005). Proximity of the residence to major roads has been used as a surrogate for exposure to traffic related air pollution (e.g. Beelen et al., 2007). Land-use regression models (LUR) therefore typically use road or traffic density as a predictor of local concentrations. Combined with other variables such as population density LUR provide a quick and accurate assessment of concentrations useful for exposure assessments (Briggs et al., 1997, Hoek et al., 2008). Other authors have used either measurements or models to demonstrate that exposure during commuting could make a significant contribution to total exposure (e.g. Fruin et al., 2004). Recent reviews of both approaches can be found in Boogaard et al. (2009) and Beckx et al. (2009a).
This increased exposure in traffic is a consequence of the fact that vehicles typically emit high quantities of pollutants under a limited number of specific driving conditions (Int Panis et al., 2006, Beusen et al., 2009). Close proximity to traffic therefore leads to peak exposure when trailing vehicles or cyclists cross the tailpipe plume. At this moment it is not clear what the health effects of short bursts of high exposure are relative to the effects of chronic exposure which are well known from epidemiological studies. Nevertheless some observations suggest that short episodes of high exposure can potentially account for some of the observed health effects (Pekkanen et al., 2002, McCreanor et al., 2007, Strak et al., 2010).
Recent advances in the field of exposure modelling have enabled the estimation of the time spent in traffic using different modes by using activity-based traffic models, a new class of transport demand models (Beckx et al., 2009b, Beckx et al., 2009c). Similar work by Marshall (2008) used data from activity diaries to estimate exposure but neither approach has been fully validated (Beckx et al., 2009c). Hence accurate assessments of exposure in different vehicles are necessary to validate model predictions so that future studies can take dynamic exposure during commuting into account (Int Panis, 2010).
In this paper we present the results of measurements of concentrations of particulate matter inside a car and on a bicycle. Ventilatory parameters are simultaneously measured to assess the amount of pollutants actually inhaled during each trip. Only a few studies (van Wijnen et al., 1995, Rank et al., 2001, O’Donoghue et al., 2007, Zuurbier et al., 2009) have taken into account that cyclists have a variable and increased minute ventilation compared to other commuters, influencing their inhaled dose of air pollutants. For this study we also explicitly want to relate the lung deposited dose to cycling intensity.
Section snippets
Study design and routes
The study described in this paper was conducted within the framework of the SHAPES project. The working hypothesis was that PM concentrations would be higher in the car than on the bicycle. This hypothesis was based on the results of a pilot study and published results from other studies (Kingham et al., 1998, Adams et al., 2001, Adams et al., 2002, Kaur et al., 2005, Kaur et al., 2007). A similar result was expected for particle number concentrations (PNC) (Kaur et al., 2005, Boogaard et al.,
General results
Table 2 summarizes some descriptive statistics about the tested persons and their performance during the experiments. Mean age (F-test, p = 0.1899) and mean BMI (F-test, p = 0.8642) were similar in all 3 locations. Mean age and sex ratio were similar to those of frequent commuter cyclists in Belgium (mean age ± SD = 39.7 ± 10; 68.2% Men, 31.8% Women; N = 932). Time based cycling speeds recorded in Brussels were somewhat lower than in both rural towns because of traffic lights and pedestrians. Otherwise
Discussion
In general, observed PNC for Brussels are similar to published numbers for cities such as Aberdeen (Dennekamp et al., 2002), Copenhagen (Vinzents et al., 2005) and a number of Dutch cities (Boogaard et al., 2009).
We have identified an apparent inconsistency in the measured average PNC values which are significantly higher in the car in Mol, but not in Brussels (at relatively higher concentrations) or LLN (with lower concentrations). This inconsistency indicates that there are differences
Conclusion and further research
The aim of the present study was to objectively compare the exposure to traffic exhaust for car passengers and cyclists. PNC, PM2.5 and PM10 and ventilatory parameters were therefore continuously measured in the field, using portable devices. From the results, we conclude that the size and magnitude of differences in concentrations depend on the location, confirming similar inconsistencies reported in literature. In Brussels and LLN, the PM2.5 and PM10 concentration was significantly higher for
Acknowledgement
The work reported in this paper was partly financed by the Belgian science policy under the Science for Sustainable Development program (project no: SD/HE/03). The authors wish to thank 3 anonymous reviewers for their suggestions as well as all test persons, Sam Vloemans, Rob Brabers, Zita Burion and Floris Huyben for their help with the data collection. Rudi Torfs contributed to the objectives of SHAPES and the design of this particular experiment in many fruitful discussions.
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