This use case will enable exposure analysis across a wide range of environmental data at continental or global scales by providing a catalogue for harmonized environmental exposure data to characterize the entire world population’s exposome. Please see our webpage to find out more about specifics of the use case and research we do with partners.
The data space will give an assessment of human exposures to environmental hazard factors, such as pollution or intense heat, on continental or global scale, to quantify relations between exposures and health outcomes as well as for the exploration of intervention policies.
Use case description
Environmental variables such as air pollution, noise, floods, green space and conflict , shape human health and disease. An important concept is the human exposome, the totality of an individual’s exposure to the environment over their lifetime. The exposome can explain a large proportion of our health, yet quantification across the world population remains surprisingly limited. As we are facing global climate and population change, it becomes increasingly important to understand and quantify the exposome. Applications for these quantifications are health research, decision-makers for adaptation strategies as well as humanitarian aid organisations. Harmonized global scale quantitative assessments of the entire exposomes of the world population remain limited, due to the heterogeneity, size and storage requirements of the required input data and compute demands in creating personal exposure estimations at high resolution.
In the pilot phase, we will work towards a minimum viable product demonstrating the technical setup and working for the Global Environmental Exposure Dataspace (GEESE). We are collecting datasets on environmental variables for a wide range of geo-domains at high resolution (<=1-10 km2) with global coverage. While incorporating population density and human mobility characterizations, exposure datasets are calculated and supplied. The pilot will focus on a limited set of variables for each of seven geo-domains. The software implementation of the processing workflow will use the open-source LUE environmental modelling framework . This is a software framework tailored to the construction of HPC-ready environmental models. The framework allows us to calculate exposures for global-scale datasets at high-resolution, using buffer operations to represent human mobility.
End users and stakeholders
GEESE addresses diverse users, including governmental bodies, health organizations, universities, insurance companies, consultancy firms, NGOs, and environmental organizations. Providers of GEESE data comprise universities, EU Destination Earth, research institutes, and consultancy firms. Stakeholders often serve in dual roles as data users and providers within the GEESE ecosystem.
The SAGE GDDS will provide the end user with a single-entry point providing search facilities and access to environmental data for exposure assessment. The data will be based on continental or global scale high resolution input data. Each of the provided environmental factors will be generated by a consistent, reproducible workflow.
Objectives/Benefits:
- Currently no data products are available providing personal exposures at a global scale. GEESE will be first in providing a complete set of all relevant human environmental exposures, produced in a harmonized manner, such that multiple exposures can be used by data consumers in an integrated manner for assessment of environmental effects on health outcomes.
- Participants in the GEESE will improve the credibility of their research or policy making as they rely on state-of-the-art exposure data. State-of-the-art in terms of spatial resolution, sophistication of data production, validation of data, as well as reproducibility of data.
- Data users with limited geocomputational skill will be able to use environmental data, as the GEESE will provide geospatial data retrieval tools, which will enable spatially specific downloading, thereby preventing users to download global datasets and extensive postprocessing steps.
Partners/Participants
| Partner | Logo | Country |
|---|---|---|
| UNIVERSITEIT UTRECHT |  | NL |
| SURF BV |  | NL |
Expected outcomes/impact
After the pilot phase, we will work towards the implementation of a larger set of variables as well smooth datastorage and connection with the catalogue.
The envisioned approach is one where a single computational framework is used to estimate personal environmental exposures from the complete set of environmental factor data. This computational framework runs in the backend of the data space and takes existing environmental hazard maps (often available in the public domain) as input and converting these to personal exposures, taking into account the fact that exposures are integrated over the spatial-temporal activity context of persons, implemented as spatial buffer operations.
These personal exposure data sets are then made available through the GEESE Data Space. In this setup, data consumers get access to a harmonized data set that includes all relevant environmental factors, processed in one single standardized way. It also allows data consumers to suggest new data sets to be included in the processing chain, such that user data can be integrated with exposure data already available in the data space.
Expected results
- The objective is to harmonize the processing of environmental data into personal exposure data through a universal computational framework running at the back end of the data space.
- The objective is to offer personal exposure through a searchable catalogue, where environmental data sets can be found; data can be downloaded for particular geographical locations as well as over particular geographical areas or time spans.
For the GEESE we expect: - An automated and harmonized assessment of personal exposures at global scale instead of ad-hoc processing of data which leads to products that are not standardised or reproducible, a less efficient workflow
- User community of data providers and data consumers leading to more efficient sharing of data.
- Geospatial data retrieval tools which will further enable better tailored data supply, also to data users that are not geocomputationally skilled.
Coordinating institutes:
Utrecht University and SURF
Kuipers, E.R.
e.r.kuipers@uu.nl
