• To develop a framework of modelling tools for estimating exposure of individuals and selected population groups
• To collect human exposure data for the ICARUS participating cities
• To adequately account for socio-economic status (SES) differences in exposure assessments
As a first step, suitable candidate sensor technologies to be used by volunteers will be selected based on the reviews undertaken in WP1 and preliminary trials of instrument reliability and utility. At the same time a data collection tool will be developed in order to store and manage all data coming from different devices. As a next step, agent-based modelling (ABM) will be used, informed from the collected multi-sensor data to capture individual spatio-temporal behaviours and to model individual exposure. It is also important to investigate the evidence of relationships between socio-economic status (SES) and exposure to pollutants. As people spend most of the time indoors, indoor concentrations will be estimated by taking into account outdoor pollutants penetrating into the room as well as important indoor sources including wood stoves and cooking with gas. Particle deposition in the pulmonary system will also be modelled so as to provide a biological metric to estimate adverse health effect. Taking into account individual exposure profiles and using the concentration-response functions established by WHO in the HRAPIE project, health impact can be assessed for a series of air pollutants in each city participating in ICARUS.
Task 4.1 Collection of multi-sensor data for personal exposure monitoring
Nowadays smartphone apps, wireless devices and the downsizing of monitoring technologies and costs make it possible for various environmental stressors and exposure factors to be measured more easily and frequently, thus providing a more reliable “time–geography of exposure” shifting the current paradigm from a population to an individual level. Ideally the sensors to be used by participating volunteers would include GPS, personal movement/activity and intensity of activity detection, environmental temperature, relative humidity and visible light. The possibilities for monitoring air pollutants (e.g. particulate matter, ozone, oxides of nitrogen), toxic organic compounds/PAHs, dioxins, furans, will also be explored using and adapting smartphone technology, commercial sensors and academic technological developments. A multi-platform data collection tool will be developed in order to store and manage all data coming from different devices.
Task 4.2 Use of ABM to derive activity patterns and exposure profiles
This task aims at deriving exposure profiles representative of population subgroups, which will be used in Task 4.4 to quantitatively estimate the associated adverse health impact. Personal sensors will be used to inform an ABM platform. ABM is a modelling technique that simulates the actions and interactions of autonomous software objects, the “agents”, enabling a better understanding of the behaviour of individuals and populations in social and evolutionary settings. The agents (which can be people, vehicles, roads, cities, animals, products, etc.) are programmed to react and act in their environment and to have goals that they aim to satisfy. An agent based model requires many simulations to evaluate any particular situation as it is based upon an underlying stochastic model. By modelling agents individually, the full effects of the diversity that exists among agents in their attributes and behaviours can be observed as it gives rise to the behaviour of the system as a whole. Patterns, structures, and behaviours emerge that were not explicitly programmed into the models, but arise through the agent interactions enabling therefore the prediction and examination of expected and unexpected/emerged behaviours.
By storing data in a geographic information system (GIS) format and using geographically explicit ABM architecture, the trajectory of an individual participant, “agent”, will be modelled and projected on a single layer, superposed onto urban air quality modelled maps of major pollutants that will derive from WP3 (Task 3.1). Personal exposure as well as inhale adjusted exposure to air pollutants will be evaluated by assigning pollutant concentrations to a person depending on different coordinates, different activities, the level of intensity and the corresponding inhalation rate.
Task 4.3 Influence of Socio Economic Status (SES)
Individual health and well-being are influenced by many factors including past and present behaviour, health care provision and ‘wider determinants’ including social, cultural and environmental factors. It can be argued that socioeconomic factors are as important as the physical environment in determining health impacts on human populations, since a disproportionate share of the burden of environmental exposure falls on vulnerable groups of society (defined as low SES, ethnic minorities, the elderly and young) due partly to issues of environmental (in)justice. Informed by evidence on the relationship between SES and exposure to pollutants (i.e. how time-activity patterns, indoor pollution and concentrations in microenvironments change with changing SES), using data collected in WP2 on air pollution levels, and existing population data, this task will evaluate how SES should be taken into account when modelling exposure. We will use geospatial techniques to distribute the exposure estimates across society at the local neighbourhood scale, for all of Europe, before utilising an Agent-Based-Model (ABM) to capture SES dynamics as well as information on the prevalence of specific behaviours in subgroups. By estimating the daily time-activity patterns of vulnerable groups, individual exposure profiles will be modelled so as to investigate the evidence of relationships between socio-economic status and exposure to pollutants (i.e. to investigate how time-activity patterns, indoor pollution and concentrations in microenvironments change with changing SES).
Task 4.4 Estimating health effects at the individual and community level
Aiming at a more precise translation of the environmental exposure into health effects, various tools will be used to improve the health impact assessment. The concentration-response functions established by WHO in the HRAPIE project will be used for the health impact assessment of the major air pollutants (PM, CO, NO2, O3, BaP), but also of dioxins and furans as a starting point. Innovations beyond the state of the art will be incorporated taking into account (a) the integral of indoor/outdoor and in transit exposure and (b) when available, more refined methodologies that estimate intake and internal dose. These include the use of human respiratory tract deposition of particles accounting for their PAHs content and the use of a biokinetic model coupled to biology based dose response relations for toxic organic pollutants such as benzene, dioxins, furans – also by quantifying the bioavailable fraction of organic pollutants (using synthetic lung fluids for extraction, and determining the mass size distribution. By refining the methodologies of health impact assessment, we will be able to better translate the effect of different options into health and monetary impact.
Already existing human biomonitoring data from population cohorts in the participating cities will be used to validate the human uptake model and, consequently, the novel health impact assessment methodology coined in ICARUS.
|Deliverable Number||Deliverable Title||Lead beneficiary||Type||Dissemination level||Due Date (in months)|
|D4.1||Report on the methodology for estimating individual exposure||1 – AUTH||Report||Public||20|
|D4.2||Report on methodology
for properly accounting for SES in exposure assessment
|3 – UNIBRIS||Report||Public||24|
|D4.3||Report on the methodology for estimating health effects
of individuals or population groups and health impact results in the ICARUS participating cities
|1 – AUTH||Report||Public||24|
|D4.4||Multi-sensor data collection IT platform for
personal exposure monitoring data fusion
|1 – AUTH||Report||Confidential, only for members of the consortium (including the