1. ^aDepartment of Preventive Medicine, Division of Environmental Health, University of Southern California, Los Angeles, California, USA
2. ^bInstitute of Social and Preventive Medicine, University of Basel, Basel, Switzerland
3. ^cEnvironmental Health, Science and Policy, University of California at Irvine, Irvine, California, USA
4. ^dDepartment of Environmental Hygiene, KTL, Kuopio, Finland
5. ^eImperial College, London, UK
6. ^fICREA and Institut Municipal d'Investigació Medica (IMIM), Barcelona, Spain

Correspondence: Nino Künzli, M.D., Ph.D., ICREA Research Professor, Center for Research in Environmental Epidemiology (CREAL), Institut Municipal d'Investigació Medica (IMIM), C. Doctor Aiguader, 80, 08003 Barcelona, Spain. Tel.: +34 93 221 10 09; Fax: +34 93 221 64 48; E-mail: kuenzli@imim.es

Received 27 April 2005; Accepted 27 March 2006; Published online 17 May 2006.

Abstract

Personal exposure to environmental substances is largely determined by time–microenvironment–activity patterns while moving across locations or microenvironments. Therefore, time–microenvironment–activity data are particularly useful in modeling exposure. We investigated determinants of workday time–microenvironment–activity patterns of the adult urban population in seven European cities. The EXPOLIS study assessed workday time–microenvironment–activity patterns among a total of 1427 subjects (age 19–60 years) in Helsinki (Finland), Athens (Greece), Basel (Switzerland), Grenoble (France), Milan (Italy), Prague (Czech Republic), and Oxford (UK). Subjects completed time–microenvironment–activity diaries during two working days. We present time spent indoors — at home, at work, and elsewhere, and time exposed to tobacco smoke indoors for all cities. The contribution of sociodemographic factors has been assessed using regression models. More than 90% of the variance in indoor time–microenvironment–activity patterns originated from differences between and within subjects rather than between cities. The most common factors that were associated with indoor time–microenvironment–activity patterns, with similar contributions in all cities, were the specific work status, employment status, whether the participants were living alone, and whether the participants had children at home. Gender and season were associated with indoor time–microenvironment–activity patterns as well but the effects were rather heterogeneous across the seven cities. Exposure to second-hand tobacco smoke differed substantially across these cities. The heterogeneity of these factors across cities may reflect city-specific characteristics but selection biases in the sampled local populations may also explain part of the findings. Determinants of time–microenvironment–activity patterns need to be taken into account in exposure assessment, epidemiological analyses, exposure simulations, as well as in the development of preventive strategies that focus on time–microenvironment–activity patterns that ultimately determine exposures.