Commuters’ Exposure to Particulate Matter Air Pollution Is Affected by Mode of Transport, Fuel Type, and Route

Public Health Services Gelderland Midden, Arnhem, the Netherlands.
Environmental Health Perspectives (Impact Factor: 7.98). 02/2010; 118(6):783-9. DOI: 10.1289/ehp.0901622
Source: PubMed


Commuters are exposed to high concentrations of air pollutants, but little quantitative information is currently available on differences in exposure between different modes of transport, routes, and fuel types.
The aim of our study was to assess differences in commuters' exposure to traffic-related air pollution related to transport mode, route, and fuel type.
We measured particle number counts (PNCs) and concentrations of PM2.5 (particulate matter <or= 2.5 microm in aerodynamic diameter), PM10, and soot between June 2007 and June 2008 on 47 weekdays, from 0800 to 1000 hours, in diesel and electric buses, gasoline- and diesel-fueled cars, and along two bicycle routes with different traffic intensities in Arnhem, the Netherlands. In addition, each-day measurements were taken at an urban background location.
We found that median PNC exposures were highest in diesel buses (38,500 particles/cm3) and for cyclists along the high-traffic intensity route (46,600 particles/cm3) and lowest in electric buses (29,200 particles/cm3). Median PM10 exposure was highest from diesel buses (47 microg/m3) and lowest along the high- and low-traffic bicycle routes (39 and 37 microg/m3). The median soot exposure was highest in gasoline-fueled cars (9.0 x 10-5/m), diesel cars (7.9 x 10-5/m), and diesel buses (7.4 x 10-5/m) and lowest along the low-traffic bicycle route (4.9 x 10-5/m). Because the minute ventilation (volume of air per minute) of cyclists, which we estimated from measured heart rates, was twice the minute ventilation of car and bus passengers, we calculated that the inhaled air pollution doses were highest for cyclists. With the exception of PM10, we found that inhaled air pollution doses were lowest for electric bus passengers.
Commuters' rush hour exposures were significantly influenced by mode of transport, route, and fuel type.

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    • "The PM2.5 concentration to which car drivers (passengers) are exposed to was set a factor 1.5 higher than the reported PM2.5 concentration by monitoring stations (Rabl and de Nazelle, 2012). The PM2.5 concentration for car drivers relative to cyclists was set at 1.24 based on a meta-analysis (Rojas-Rueda, 2012) from studies in London (Adams et al., 2001a; Adams et al., 2001b; Kaur et al., 2005), Barcelona (De Nazelle et al., (2011)), Arnhem (Zuurbier et al., 2010) and several Dutch cities (Boogaard et al., 2009). An identical approach was taken for walking. "
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    ABSTRACT: In Flanders, a European hot spot for air pollution, alternatives to car transport are put in place to increase the daily level of physical activity (PA) among the population and reduce air pollution and global warming. To evaluate the economic impact of increased PA (cycling and walking), a health impact model was developed for a given volume of PA, relative to car use, within a defined population in Flanders. Flanders is an interesting region because of the combination of high air pollution, high cycling volumes and good data availability e.g on crashes and PA. The model uses two health indicators: external costs and DALYs. Considered impacts in the model are: mortality and morbidity related to increased PA, air pollution exposure for society and active individuals and crash risks. In addition to health, external costs for CO2 emission, congestion and noise exposure can be considered. The model was applied to the new bicycle highways Antwerp-Mechelen and Leuven-Brussels, which were built near important traffic axes to provide the densely populated region with an alternative to car use. Different sensitivity analyses with a variable number of cyclists and travelled distances were elaborated to check the robustness of the results. Overall, the conclusion was that increased PA outweighed other impacts. The benefit:cost ratio for health impact and infrastructure construction costs was mainly positive, even with conservative assumptions and when the impacts of congestion, noise and reduced CO2 were not accounted for. When reduced congestion was added to the model, benefit:cost ratios largely exceeded one. The model can be used in a retrospective way to analyse previous investments or can be applied to new policy decisions. The presented model is tailored here to the Flemish context for crash risks and air pollution but parameters can easily be adapted to reflect conditions in other regions. Model: Article already available at:
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    • "). This exposure is related to the transportation mode; car users tend to experience the highest exposure compared to walkers, bus or bike users (de Nazelle et al., 2012; Zuurbier et al., 2010 "
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    ABSTRACT: Studies of air pollution effects during pregnancy generally only consider exposure in the outdoor air at the home address. We aimed to compare exposure models differing in their ability to account for the spatial resolution of pollutants, space-time activity and indoor air pollution levels. We recruited 40 pregnant women in the Grenoble urban area, France, who carried a Global Positioning System (GPS) during up to 3weeks; in a subgroup, indoor measurements of fine particles (PM2.5) were conducted at home (n=9) and personal exposure to nitrogen dioxide (NO2) was assessed using passive air samplers (n=10). Outdoor concentrations of NO2, and PM2.5 were estimated from a dispersion model with a fine spatial resolution. Women spent on average 16h per day at home. Considering only outdoor levels, for estimates at the home address, the correlation between the estimate using the nearest background air monitoring station and the estimate from the dispersion model was high (r=0.93) for PM2.5 and moderate (r=0.67) for NO2. The model incorporating clean GPS data was less correlated with the estimate relying on raw GPS data (r=0.77) than the model ignoring space-time activity (r=0.93). PM2.5 outdoor levels were not to moderately correlated with estimates from the model incorporating indoor measurements and space-time activity (r=-0.10 to 0.47), while NO2 personal levels were not correlated with outdoor levels (r=-0.42 to 0.03). In this urban area, accounting for space-time activity little influenced exposure estimates; in a subgroup of subjects (n=9), incorporating indoor pollution levels seemed to strongly modify them. Copyright © 2015 Elsevier Ltd. All rights reserved.
    Environment international 08/2015; 84:161-173. DOI:10.1016/j.envint.2015.07.021 · 5.56 Impact Factor
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    • "The most common anthropogenic source contributing particulate matter (PM) to the urban atmosphere is the vehicular emissions. It has gained the importance due to occupational exposure for drivers and public exposure for the people using these vehicles for transportation and road sides for daily activities (Devi et al., 2009; Zuurbier et al., 2010; Chio et al., 2012). "
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