Exposure of population and microenvironmental distributions of volatile organic compound concentrations in the EXPOLIS study

Saarela, Kristina; Tirkkonen, Tiina; Laine-Ylijoki, Jutta; Jurvelin, Jouni; Nieuwenhuijsen, Mark; Jantunen, M.

doi:10.1016/j.atmosenv.2003.09.031

Cited by 66 publications

(41 citation statements)

References 10 publications

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“…It is important to increase ventilation when using products that emit VOCs (Yoon et al 2004). Previous studies have found that outdoor sources such as petroleum hydrocarbons and vehicle exhaust may contribute significantly to indoor pollution (Keady and Mainquist 2000;Saarelaa et al 2003). Several efforts to identify and control emissions from these possible sources have been undertaken and various models may help to predict possible sources (Guo 2002;Choi 1997).…”

Section: Discussionmentioning

confidence: 97%

Indoor air quality differences between urban and rural preschools in Korea

Yoon

Lee

Park

2010

Environ Sci Pollut Res

118

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We confirmed that pollutants in indoor and urban settings were higher than those in outdoor and rural areas, respectively. Preschool children are expected to spend more time inside preschool facilities and therefore to be more exposed to pollutants. As far as we know, preschool IAQ is different from elementary school or higher school IAQ. Also, they are more vulnerable than higher-grade children. We found that the indoor and urban concentration profiles of the studied pollutants in preschools were higher than those in outdoor and rural areas. We believe that our findings may be useful for understanding the potential health effects of exposure and intervention studies in preschools.

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Section: Discussionmentioning

confidence: 97%

Indoor air quality differences between urban and rural preschools in Korea

Yoon

Lee

Park

2010

Environ Sci Pollut Res

118

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“…Recently, the scientific interest related to benzene exposure assessment has mainly focused on the determination of the parameters that determine the overall amount of exposure in relation to specific activities and selected microenvironments [14,15,32,37,38,41]. Beside some differences in the methodological approach, in all these studies, the importance of the parameters defining exposure was assessed by combining the measurement results and the evaluation of the information included in questionnaires via several statistical methods.…”

Section: Introductionmentioning

confidence: 99%

Exposure Modeling of Benzene Exploiting Passive–Active Sampling Data

Karakitsios

Kassomenos

Sarigiannis

et al. 2009

Environ Model Assess

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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.

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“…These patterns point to the importance of the contribution of indoor sources to indoor concentrations in these microenvironments. Although measurement studies have found that much of personal exposure can be explained by home, work, and outdoor exposures, some unexplained variability still remains (Wallace et al, 1991;Saarela et al, 2003). Some of this unexplained variability, especially for certain population subgroups, might be described by exposures in stores and restaurants or bars.…”

Section: Introductionmentioning

confidence: 99%

Contribution to volatile organic compound exposures from time spent in stores and restaurants and bars

Loh

Houseman

Levy

et al. 2008

J Expo Sci Environ Epidemiol

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Many people spend time in stores and restaurants, yet there has been little investigation of the influence of these microenvironments on personal exposure. Relative to the outdoors, transportation, and the home, these microenvironments have high concentrations of several volatile organic compounds (VOCs). We developed a stochastic model to examine the effect of VOC concentrations in these microenvironments on total personal exposure for (1) non-smoking adults working in offices who spend time in stores and restaurants or bars and (2) non-smoking adults who work in these establishments. We also compared the effect of working in a smoking versus non-smoking restaurant or bar. Input concentrations for each microenvironment were developed from the literature whereas time activity inputs were taken from the National Human Activity Patterns Survey. Time-averaged exposures were simulated for 5000 individuals over a weeklong period for each analysis. Mean contributions to personal exposure from non-working time spent in stores and restaurants or bars range from o5% to 20%, depending on the VOC and time-activity patterns. At the 95th percentile of the distribution of the proportion of personal exposure attributable to time spent in stores and restaurants or bars, these microenvironments can be responsible for over half of a person's total exposure to certain VOCs. People working in restaurants or bars where smoking is allowed had the highest fraction of exposure attributable to their workplace. At the median, people who worked in stores or restaurants tended to have 20-60% of their total exposures from time spent at work. These results indicate that stores and restaurants can be large contributors to personal exposure to VOCs for both workers in those establishments and for a subset of people who visit these places, and that incorporation of these non-residential microenvironments can improve models of personal exposure distributions.

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Exposure of population and microenvironmental distributions of volatile organic compound concentrations in the EXPOLIS study

Cited by 66 publications

References 10 publications

Indoor air quality differences between urban and rural preschools in Korea

Indoor air quality differences between urban and rural preschools in Korea

Exposure Modeling of Benzene Exploiting Passive–Active Sampling Data

Contribution to volatile organic compound exposures from time spent in stores and restaurants and bars

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