## 4 Conclusions and Discussion  
To assess personal health effects of air pollution, two steps are essential: to quantify personal ambient air pollution concentration, exposure duration and pollutant intake, and to evaluate the related personal health effects by considering personal space-time behavior. This paper showcases how an individual-level assessment of air pollution exposure and health effects can be evaluated through systematically integrating personal level data collection, space-time modeling, and geovisualization technologies. First, space-time trajectory information must be added to air pollution data in order to depict where, when, and how much an individual is exposed to air pollution. Real-time air pollution concentration data can be collected with ease using a portable air pollutant sensor. But they cannot be linked directly to personal space-time behavior. To solve this problem, the individual space-time trajectory data was collected by a portable GPS unit and was linked to air pollution data. Second, a 3D modeling and visualization approach was developed based on time geography framework to model a space-time cube, a space-time path within the cube, and the dynamics of individual-environment interaction along the path. The environment variation within a space-time cube and the dynamics of individual-environment interaction along a space-time path are not accounted for by the traditional time geography. This study models the space-time path as a collection of qua-tuples, \(I\left(x_{i},y_{i},t_{i},E_{i}\right)\), where \(E_{i}\) represents environmental exposure at space-time point \(i\). In the case of air pollution and individual health effect, the environmental exposure can be defined by the air pollutant concentration level and the pollutant intake rate (which is related to personal behavior and individual physical situation) along the space-time path. The approach of  
Figure 6: The personal air pollution danger zones for the volunteer in Houston, Texas on 27 and 28 December 2010.  
qua-tuples in space-time cube illustrates an effective measure to address the uncertain geography context problem (UGCoP) as identified by [PERSON] [31]. Third, based on the above two advancements, the proposed approach in this paper assesses personal level health effects of air pollution by considering air pollutant intake of an individual, which is determined by air pollution concentration, personal space-time trajectory, and personal activities throughout the trajectory. Air pollutant concentration levels in an individual's immediate surroundings were converted to space-time path AQI values (Figure 4); air pollutant intake was estimated using information from personal travel diary (Figure 5); personal air pollution health danger zones (Figure 6) were identified by considering air pollution and personal space-time behavior. An individual's air pollution exposure changes through space and time, and it changes differently from his/her pollutant intake. In the case study, the O\({}_{3}\) concentration level for the volunteer's ambient air showed some regularity. When the volunteer drove along major roads, the O\({}_{3}\) concentration was medium to high level; when he stayed inside and walked/jogged in shopping/residential areas, the concentration remained low. This pattern may suggest the important role of traffic emission to elevated O\({}_{3}\) levels. Compared to O\({}_{3}\) concentration, the volunteer's O\({}_{3}\) intake rates varies largely (_i.e._, ranging from \(2\times 10^{-7}\) L/min to \(30\times 10^{-7}\) L/min). Figures 5 and 6 revealed that the volunteer's O\({}_{3}\) intake had six peaks, indicating air pollution health danger zones. Five of the danger zones (zone 1, 2, 4, 5, and 6) are in the downtown area of Houston and one (zone 3) in Galveston, Texas. The downtown danger zones mostly highlight the volunteer's walking activities, and the Galveston danger zone represents the volunteer's running activity.