Name EcoSense
Acronym EcoSense
Methodical Focus Environmental impact assessment , Air quality management , External costs
Institution(s) Institute of Energy Economics and Rational Energy Use; University of Stuttgart
Author(s) (institution, working field, active time period)
Current contact person Dorothea Schmid
Contact (e-mail) dorothea.schmid@ier.uni-stuttgart.de
Website https://www.ier.uni-stuttgart.de/en/research/models/ecosense/
Logo
Primary Purpose EcoSense is a Europe-wide integrated atmospheric dispersion and exposure assessment model which calculates external costs related to the exposure to airborne pollutants with a focus on impacts on human health, following the Impact Pathway approach. EcoSense considers impacts of classical air pollutants, such as SO2, NOx, Particulate Matter, NMVOCs and NH3 and assesses impacts due to long-term exposure to particulate matter (PM), nitrogen dioxide (NO2), and ozone (SOMO35).
Primary Outputs EcoSense outputs health impacts with the common metric "Disability Adjusted Life Years" (DALY). DALYs consider both changes in quality of life (due to illness) and losses in life expectancy (due to premature deaths). Impacts can also be monetized, which results in estimates of external costs.
Support / Community / Forum
Framework Impact Pathway Approach
Link to User Documentation https://openenergy-platform.org/dataedit/view/scenario/reeem_ecosenseeva_output
Link to Developer/Code Documentation -
Documentation quality not available
Source of funding Third-party-funded research
Number of developers less than 10
Number of users -
Open Source
License Creative Commons Attribution 4.0
Source code available
GitHub
Access to source code -
Data provided all data
Collaborative programming
Modelling software PHP, Python, R
Internal data processing software SQL
External optimizer
Additional software
GUI
Modeled energy sectors (final energy) -
Modeled demand sectors -
Modeled technologies: components for power generation or conversion
Renewables -
Conventional -
Modeled technologies: components for transfer, infrastructure or grid
Electricity -
Gas -
Heat -
Properties electrical grid -
Modeled technologies: components for storage -
User behaviour and demand side management
Changes in efficiency
Market models -
Geographical coverage EU28; Norway; Switzerland
Geographic (spatial) resolution national states, up to 25' x 25'
Time resolution annual
Comment on geographic (spatial) resolution spatial resolution can be defined by users, highest resolution currently possible is 25' x 25' (determined by population data)
Observation period 1 year
Additional dimensions (sector) -
Model class (optimisation) -
Model class (simulation) Top down
Other
Short description of mathematical model class
Mathematical objective costs, external costs of emission reduction scenarios
Approach to uncertainty comperative scenario analysis
Suited for many scenarios / monte-carlo
typical computation time less than an hour
Typical computation hardware -
Technical data anchored in the model -
Interfaces
Model file format Other
Input data file format .csv
Output data file format .csv
Integration with other models Soft-link with energy system optimization models (air pollutants; damage cost factors)
Integration of other models Soft-link with atmospheric dispersion models
Citation reference -
Citation DOI -
Reference Studies/Models https://doi.org/10.5281/zenodo.3368530
Example research questions -
Model usage -
Model validation cross-checked with other models
Example research questions -
further properties
Model specific properties -

If you find bugs or if you have ideas to improve the Open Energy Platform, you are welcome to add your comments to the existing issues on GitHub.
You can also fork the project and get involved.

Please note that the platform is still under construction and therefore the design of this page is still highly volatile!


Actions

Edit

Tags

Europe REEEM health