Background: The exposure to air pollution and noise is severely impacting people & rsquo;s health and is especially high alongside urban road- and rail-traffic. In traditional exposure research, air pollution and noise are monitored with stationary measurement devices or based on models. During the last years, mobile measurement techniques with GPS-tracking have increased. Moreover, studies have investigated another dimension of personal exposure: the perceived exposure.

Aim: Most of these studies make use of quantitative methods such as surveys, complemented by stationary or wearable sensors. Little research exist that applies qualitative methods to examine how people experience and perceive exposure on-the-move, contrasting it to actual measurements. The aim of this paper is to discuss the potential of a novel method, which extends the dimensions of personal exposure by including the situational context of exposure perception.

Methods/case study: Firstly, different methods for exposure research are presented. Secondly, we introduce a novel mixed-method approach, exploring cyclists and pedestrians perceived and measured exposure on-the-move by combining mobile interviews (Go-/Ride-Alongs) and wearable sensors. We will present the methodological findings using a case study and have a quantitative method (smartphone questionnaire, wearable sensors) as a reference.

Results and discussion: The differences of perceived and measured exposure, proven through the reference method, are a result of different situational contexts as shown by the mobile interviews (knowledge, embodied experience, life situation, activities). The methodological findings show, that mobile methods complemented by wearables introduce new dimensions of personal exposure: they shed light on the situational contexts that affect exposure perception during commute.

Conclusion: We argue that both, perceived and measured exposure to air pollution and noise, need to be considered simultaneously. Complementing mobile interviews or surveys with wearable sensor data improves the understanding of urban dwellers requirements for healthier mobility. Potentials of these methods should be investigated further, both in research and for supporting urban transport planning decisions adapted to people & rsquo;s needs.