The basic concept of this PhD is the physical relation between the simultaneous exposure to noise and air pollution. In most noise evaluations assessing the health effects of noise on humans, the A-weighted equivalent noise LAeq or the more aggregated parameter LDEN is used. The actual physical relation between noise and PM emissions is more complex and does not fit the LAeq or LDEN. This is visually illustrated in Figure 1.2.1.
Figure 1.2.1: Structure of chapter 4 and 5. The underlying data (noise measurement or noise maps) is related to the investigated micro-environments (ME). At the right side, the matching evaluation level of the sections is shown.
The different emissions (PM, engine related noise, tyre related rolling noise and the total noise levels) are related to different driving conditions in a qualitative way. The engine re-gime (throttle) has a stronger link with the instantaneous PM emissions compared to LAeq. It is important to understand that the evaluation of the noise related health effects use differ-ent noise indicators than the indicators that will be used in this work to predict the in-traffic air pollution exposure.
1.2.2 Goals
Present a person centred approach for assessing the subjective perception based Quality of Life including evaluations at the dwelling, in traffic and at the destinations and evaluate the potential of the noise assessments as a proxy for the subjective per-ception of the traffic related quality of life.
Develop a generalized activity based and person centred data flow framework to en-able the calculation of any spatiotemporal indicator on any predefined mobile popu-lation.
Use noise exposure as a proxy for local traffic assessment to model and predict Black Carbon exposure in different micro-environments. Instantaneous micro-environment specific land-use regression models will be evaluated for three micro-environments:
bicycle, in-vehicle and indoor. These models will be referred to as µLUR (‘microscop-ic’ in time and space and micro-environment specific).
Model and evaluate the instantaneous spatiotemporal personal exposure to Black Carbon for the full time-activity pattern, including a validation with external exposure measurements.
1.2.3 Outline
The PhD builds on prior work from the traffic related Quality of Life model. The first part of the second chapter describes the multi-indicator methodology of this model. In the second part of the second chapter a more generalized indicator workflow is presented. It extends the functionality of the Quality of Life model to activity specific models and adds functionali-ty towards participatory sensing. The third chapter summarizes the results of the Qualifunctionali-ty of Life model. The focus in this chapter is on the power of the spatial information in noise maps to act as a proxy for the subjective perception of the traffic related quality of life, also re-ferred to as ‘Traffic Liveability’. Extended literature on this work and other outcomes can be found in the publication list (1.2.4).
The fourth chapter describes the noise based micro-environment specific Black Carbon exposure models. It is important to define ‘exposure’ in this context. Many authors and
dis-ciplines use similar nouns in different contexts (see also 1.1.2.3). In the presented models no dose corrections of any type (inhalation rate, deposition in lungs, etc.) are included, only the concentration at the position of the subject in space and time is modelled and evaluated.
This is referred to as ‘personal exposure’ since the personal time-activity pattern is the main driving force. Dose corrections become only relevant when the personal exposure is linked to potential health effects. This work does not extend the results to health effects and in-cludes therefor no activity related dose corrections. The general data workflow presented in 2.3 can include dose corrections. The dose corrections will be added in future applications through a multidisciplinary and project specific decision process (see section 6.1).
Figure 1.2.2: Structure of chapter 4 and 5. The underlying data (noise measurement or noise maps) is related to the investigated micro-environments (ME). At the right side, the matching evaluation level of the sections is shown.
In Figure 1.2.2, the relation and the evaluation levels of the different sections are pre-sented. It starts with an instantaneous model for the BC exposure of bicyclists (4.2). A yearly meteorology adjusted exposure and a city wide mapping methodology is presented in sec-tion 4.3. In secsec-tion 4.4, the technique is tested with an automated noise measurement setup in an international context and includes an extension from BC to UFP. In section 4.5, an in-stantaneous model for in-vehicle exposure is presented. The traffic assessment moves from noise measurements to noise maps. In section 4.6, the instantaneous model for bicyclists is merged with noise map data and is used to build an instantaneous exposure model for the indoor micro-environment. In section 4.7, the lessons learned throughout the chapter are tested in a pilot experiment using simultaneous noise and BC measurements at a dwelling facade. In the fifth chapter, the results of chapter four are combined to predict the personal daily exposure to Black Carbon (5.2). Section 4.2, 4.3 and 4.4 are published as independent research articles.
A general discussion is added, summarizing all features of the presented work (5.3). In chapter six, the future potential and applications are presented and the conclusions are summarized.