Impact of time–activity patterns on personal exposure to black carbon
Highlights
► Personal exposure monitoring with a high temporal resolution. ► Time–activity patterns are a key determinant of personal exposure to air pollution. ► Exposure between partners, living at the same address, can differ by up to 30%. ► Exposure while in transport far exceeds exposure in other microenvironments. ► This research underlines the importance of personal monitoring for health purposes.
Introduction
Personal exposure can be defined as the real exposure as it is experienced by individuals. When an individual is present at a certain place or in a certain microenvironment, he or she is exposed to the pollutant concentrations at this specific place. When an individual makes a trip from location A to location B, his personal exposure can be defined as the weighted average of concentrations present at each single location (WHO, 1999). Up till now, personal exposure is often estimated through the use of concentrations measured at fixed monitoring stations (Kaur et al., 2007, Sarnat et al., 2009). This is an approximation, as not only the ambient concentration is relevant, but also concentrations in different microenvironments (including indoors) and the whereabouts of individuals (Boudet et al., 2001, Jensen, 1999, Klepeis, 2006). Several studies have already examined the correlation between personal exposure and concentrations measured at fixed monitoring stations (Avery et al., 2010, Boudet et al., 2001, Gulliver and Briggs, 2004). This correlation shows a large spread between different studies, but overall correlation is stronger for longitudinal within-person studies, compared to cross-sectional studies (Avery et al., 2010). This indicates that differences between people and a large part of the spread within a subject can be explained by the activity pattern of the individuals and their daily environment.
Several studies are looking at the relationship between levels of exposure and health effects, but epidemiologists experience vast problems with exactly quantifying exposure. By using approximations for exposure, health effects can be wrongly assigned, or the strength of a relationship will not be sufficiently emphasized (Jerrett et al., 2005, Setton et al., 2011). Therefore researchers are looking at methods, either through direct measurements or indirect modeling, to reduce exposure misclassification (Int Panis, 2010).
We hypothesize that people, who are living at the same location, can nevertheless have a different exposure profile. The driving force for this difference will be the activity pattern and the subsequent microenvironments visited during a day. Short-term exposures may contribute significantly to daily average exposure. The aim of this study is to look at week-long exposure profiles with a high temporal resolution. Linking these data with detailed time–activity patterns will tell us what the impact is of an activity pattern on personal exposure. Two groups of people with a highly differential time–activity pattern were selected to demonstrate this.
The pollutant looked at is black carbon (BC). BC has been used as an indicator of exposure to diesel exhaust (HEI, 2010), and it has been suspected as a contributor to global warming (Highwood and Kinnersley, 2006). Several researchers have recently stressed potential short and long-term cardiovascular, respiratory and neurodegenerative health effects of BC (Baja et al., 2010, McCracken et al., 2010, Patel et al., 2010, Suglia et al., 2007). Over the last 40 years BC-concentrations have declined rapidly in Europe, although the air has still moderate to heavy BC pollution. In the last decade concentrations seem to have leveled off possibly because of increasing emissions of diesel passenger cars.
Section snippets
Study design and sampling method
Personal exposure measurements were performed in Belgium from May 2nd to July 8th 2010. 16 participants were asked to carry a device to measure BC-concentrations and to record their activities and whereabouts in an electronic diary. The study population comprises 8 couples, consisting of a full-time worker and a homemaker. Participants performed their regular activities; there were no restrictions but weeks where respondents were abroad or planned a weekend trip were excluded. All participants
Questionnaire data and time–activity patterns
The age of all sixteen participants was between 20 and 60 (since we recruited in the working population). Eight were male and eight female, with a small bias toward higher education. Everyone was in the possession of a driver’s license. One household had no private car; other households had either one or two cars, all diesel.
Table 1 shows the percentage of time spent by the participants on each activity. The initial 13 activities, plus ‘in transport’, are grouped in eight broader categories.
Discussion
For BC, our personal monitoring study did reveal an undeniable contribution from the transport microenvironment. The amount of time in transport and the transport mode are important determinants of personal exposure to BC. People living at the same location and in the same residence, as the couples in this study did, sometimes had a completely different exposure, largely explained by the difference in activity pattern and their corresponding time in transport. This confirms earlier studies on
Acknowledgments
The authors would like to thank the men and women who were willing to take part in this study. Bruno Kochan and Dirk Roox from Hasselt University are acknowledged for their work on PARROTS.
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