Exposure Modeling of Benzene Exploiting Passive–Active Sampling Data |
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Authors: | Spyros P Karakitsios Pavlos A Kassomenos Dimosthenis A Sarigiannis Georgios A Pilidis |
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Institution: | (1) European Commission (EC), Joint Research Center (JRC), Institute for Health and Consumer Protection (IHCP), Ispra (Va), 21020, Italy;(2) Department of Physics, Laboratory of Meteorology, University of Ioannina, 45110 Ioannina, Greece;(3) Department of Biological Applications and Technologies, Laboratory of Environmental Chemistry, University of Ioannina, 45110 Ioannina, Greece |
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Abstract: | The objective of the present study is the exploitation of active sampling personal exposure data in assessing the factors
that affect exposure to benzene in combination with the widely accepted scheme of passive sampling—time microenvironment–activity
diaries (TMAD). The campaign included personal exposure measurements with both passive and active sampling in several microenvironments,
evaluation of TMAD kept by the volunteers, and a variety of environmental data (ambient air benzene determination, traffic
and meteorological observations). Due to the relatively elevated benzene traffic emissions, average personal exposure was
determined to be equal to 8.9 μg/m3, ranging between 5 and 20 μg/m3, which is a value highly related to the average urban concentration (9.2 μg/m3). The information gained from TMAD was embedded (in terms of spatial and temporal distribution) into three zones respectively,
in order to draw statistically significant conclusions about the exposure levels and the activity patterns. The contribution
of the activities to the overall amount of exposure was further quantified and refined by active sampling measurements. These
data revealed that driving in a traffic-congested road was the main activity leading to elevated exposure levels (up to 70 μg/m3), followed by walking on the roadside of a congested road (up to 35 μg/m3). Indoor exposure to benzene was in general lower than outdoor (indicating that traffic is the dominant source of benzene
emissions in the wider area), and it was significantly affected by the presence of environmental tobacco smoke. The higher
significance of the regression coefficients obtained by statistical analysis of the active sampling data was fundamental for
the development of a regression-based prediction exposure model. The model was evaluated through comparison with the passive
sampling data, which were considered as an unknown but realistic data exposure pattern. The model performed very well in terms
of expressing the variance of the exposure data with an average score of R
2 equal to 0.935. All of the above indicate that active sampling is a necessary albeit more laborious tool that needs to be
used as a complement to passive sampling for precise quantification of the factors determining personal exposure patterns. |
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