Exposure Modeling of Benzene Exploiting Passive–Active Sampling Data

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/m 3 , ranging between 5 and 20 μg/m 3 , which is a value highly related to the average urban concentration (9.2 μg/m 3 ). 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/m 3 ), followed by walking on the roadside of a congested road (up to 35 μg/m 3 ). 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.

Integrated exposure for risk assessment in indoor environments based on a review of concentration data on airborne chemical pollutants in domestic environments in Europe

“…It describes the dynamics of typical concentrations and exposure variability of benzene in a Greek city. 104 External concentration variability is translated into indoor concentration variability taking into account building characteristics such as air exchange rate and building volume. Combined to time-activity dynamics of the residents, exposure and actual intake rate are also estimated in time, reflecting the actual effect that specific activities such as cooking or smoking and spending time in specific locations (e.g.…”