## 1 Introduction  
Exposure assessment is essential for determining the relationship between various environmental factors and health effects [1; 2]. One of the biggest challenges facing environmental epidemiology recently is the dynamic nature of exposure [3]. Individuals constantly move in time and space and a better understanding of time-activity patterns according to different demographic, socioeconomic and environmental variables is relevant to improve health impact assessment [4; 5]. Several studies comparing time-activity patterns and the role of individual-level characteristics on these patterns have revealed significant variations between demographic and socioeconomic factors such as age, gender, employment status, income, education, and race/ethnicity [6; 7]. Recently, [PERSON] et al. [8] have found significant differences in the daily time-activity patterns between rural and urban populations. Therefore, in order to avoid exposure measurement errors and more accurately determine the relationship between environmental factors and health effects, it is necessary to take into account time-activity patterns. Recent epidemiological research focuses on sensor technologies such as smartphones, global positioning system (GPS) devices and accelerometers to assess individual-level exposures and biological responses [9; 10]. The use of these technologies helps to capture individuals' time-activity data considering all microenvironments (MEs) in which people spend their time [11]. GPS device allows to track and to monitor people's daily movements and commuting patterns and providesinformation about geographic coordinates, time, distance, speed, altitude [12]. GPS tracking devices are used in combination with travel/activity diaries or accelerometer to improve the measurements of daily movement patterns and exposure assessment [13; 14]. GPS technology reduces the measurement bias and collects more precise data of time-activity patterns compared to traditional self-reported surveys [15]. One of the approaches to assessing human exposure is based on the concept of microenvironments suggested by [PERSON] [15]. People spend their daily time in various MEs and they are exposed to different environmental exposures in each of them, so it is necessary to classify MEs into categories. The most frequently used MEs categories for exposure studies are home, work/ school, other indoor, outdoor, and transport [16; 17; 18]. According to the World Health Organization many European citizens spend, on average, 90% of their time indoors: at home (two-thirds of this time), at work, at school and in public places [19]. A study by [PERSON] et al. [20] investigated time-activity patterns in seven regions across Europe and the results showed that more than 90% of the variance in indoor time-activity patterns originated from differences between subjects rather than between cities. Therefore, this study is more focused on indoor MEs, where people tend to spend most of their time and we aim to determine the most important factors influencing time spent indoors. Previous studies have emphasized the importance of taking into account time-activity patterns for assessing physical activity, exposure to environmental factors, and health effects, but there is a lack of research linking behavioral characteristics and environmental factors to time spent indoors. It is also important to find out which factors are most important and which subgroups of the population are more susceptible to exposure misclassification. Residential greenness and noise concentration were selected as environmental factors to evaluate how they may influence peoples' behavior and time-activity patterns. We have a preliminary hypothesis that green spaces may promote people to spend more time outdoors. On the contrary, a higher outdoor noise level can cause people to spend more time indoors. In this paper, we present the results on the use of GPS integrated into smartphones that continuously records an individual's time and location data and allows us to assess time-activity patterns.