Individual daytime noise exposure in different microenvironments Ute Kraus a,n , Susanne Breitner a , Regina Hampel a , Kathrin Wolf a , Josef Cyrys a,b , Uta Geruschkat a , Jianwei Gu a,b , Katja Radon c , Annette Peters a , Alexandra Schneider a a Institute of Epidemiology II, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany b Environment Science Center, University of Augsburg, Augsburg, Germany c Institute and Outpatient Clinic for Occupational, Social and Environmental Medicine, University Hospital Munich, Munich, Germany article info Article history: Received 8 February 2015 Received in revised form 4 May 2015 Accepted 7 May 2015 Keywords: Noise exposure Personal exposure Microenvironments Epidemiology Activity diary abstract Background: Numerous studies showed that chronic noise exposure modeled through noise mapping is associated with adverse health effects. However, knowledge about real individual noise exposure, emitted by several sources, is limited. Objectives: To explain the variation in individual daytime noise exposure regarding different micro- environments, activities and individual characteristics. Materials and methods: In a repeated measures study in Augsburg, Germany (March 2007–December 2008), 109 individuals participated in 305 individual noise measurements with a mean duration of 5.5 h. Whereabouts and activities were recorded in a diary. One-minute averages of A-weighted equivalent continuous sound pressure levels (L eq ) were determined. We used mixed additive models to elucidate the variation of L eq by diary-based information, baseline characteristics and time-invariant variables like long-term noise exposure. Results: Overall noise levels were highly variable (median: 64 dB(A); range: 37–105 dB(A)). Highest noise levels were measured in traffic during bicycling (69 dB(A); 49–97 dB(A)) and lowest while resting at home (54 dB(A); 37–94 dB(A)). Nearly all diary-based information as well as physical activity, sex and age-group had significant influences on individual noise. In an additional analysis restricted to times spent at the residences, long-term noise exposure did not improve the model fit. Conclusions: Individual exposures to day-time noise were moderate to high and showed high variations in different microenvironments except when being in traffic. Individual noise levels were greatly de- termined by personal activities but also seemed to depend on environmental noise levels. & 2015 Elsevier Inc. All rights reserved. 1. Introduction A growing body of evidence shows adverse associations be- tween chronic noise exposure and human health. Several epide- miological studies have identified noise exposure to be a major contributor to hearing loss (Sliwinska-Kowalska and Davis, 2012), sleep disturbance (Hume et al., 2012), cardiovascular disease (Davies and Kamp, 2012), impairment of performance (Clark and Sorqvist, 2012), altered endocrine responses (Babisch, 2003), mental illness as well as annoyance (Stansfeld and Matheson, 2003). Most of these associations were assessed in long-term studies, where noise was predicted through strategic noise map- ping. Thereby, these studies concentrated on noise exposure from selected sources, in particular road traffic, railway system, aircraft and occupational settings. The results of these studies provided the basis for the development of guideline values (Berglund et al., 1999; WHO, 2009) and the calculation of burden of disease in terms of disability-adjusted life-years (WHO, 2011, 2012). As a consequence, traffic noise was placed as the second most dan- gerous environmental threat to human health after air pollution in six European countries (EBoDE, 2010; Hanninen et al., 2014). However, people are usually exposed to noise from more than one source simultaneously. Also, noise levels predicted through noise mapping do not provide valid information about individual ex- posure. To date, only a few studies measured noise continuously and were able to describe noise levels in specific microenviron- ments or during different activities (Boogaard et al., 2009; Clark, 1991; Diaz and Pedrero, 2006; Flamme et al., 2012; Neitzel et al., 2004b; Neitzel et al., 2014; Weinmann et al., 2012; Zheng et al., Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/envres Environmental Research http://dx.doi.org/10.1016/j.envres.2015.05.006 0013-9351/& 2015 Elsevier Inc. All rights reserved. Abbreviations: CI, confidence interval; dB(A), A-weighted decibels; KORA, Co- operative Health Research in the Region of Augsburg; L day , Maximum annual A-weighted equivalent continuous sound pressure levels during the day (6 am to 6 pm); L eq , A-weighted equivalent continuous sound pressure levels; LOD, limit of detection; PNC, particle number concentration; R 2 , coefficient of determination; sd, standard deviation; VIF, variance inflation factor; WHO, World Health Organization n Correspondening author. Fax: þ49 89 3187 3380. E-mail address: ute.kraus@helmholtz-muenchen.de (U. Kraus). Environmental Research 140 (2015) 479–487