Here you find the code to run the SHERPA Source Receptor Relationship, for simulating the impact on air quality of emission reduction scenarios, in EU regions and cities.
The code is provided to show how the SHERPA Graphical User Interface (GUI) works behind the scene. SHERPA GUI can be found at https://jeodpp.jrc.ec.europa.eu/eu/dashboard/voila/render/SHERPA/Sherpa.ipynb.
On top of using it in the GUI, this python code can also be used as a 'stand-alone' code, to simulate the impact of emission reduction scenarios on air quality, and to perform source allocation studies.
In this repository there is also a Jupyter notebook ('how_to_run_module1.ipynb') explaining how to use the 'scenario analysis' module directly in python (explaining how to set-up a python environment, and then how to run the SHERPA scenario analysis).
Input data to run SHERPA over Europe are provided in this same repository. More details on the data are avaiable in the next sections, and in the provided Jupyter Notebook.
SHERPA (Screening for High Emission Reduction Potential on Air) is tool, which allows for a rapid exploration of potential air quality improvements resulting from national/regional/local emission reduction measures. The tool has been developed with the aim of supporting national, regional and local authorities in the design and assessment of their air quality plans.The tool is based on the relationships between emissions and concentration levels, and can be used to answer the following type of questions:
- What is the potential for local action in my domain?
- What are the priority activity, sectors and pollutants on which to take action and,
- What is the optimal dimension that my policy action domain (city, region…) should have to be efficient?"
The SHERPA tool is distributed with EU-wide data on emissions and source-receptor models (spatial resolution of roughly 6x6 km2), so that it is very easy to start working on any region/local domain in Europe.
More specifically, SHERPA logical pathway is implemented through the following steps:
- Source allocation: to understand how the air quality in a given area is influenced by different geographical and sectoral sources;
- Scenario analysis: to simulate the impact on air quality of a specific emission reduction scenario (defined also through the previous two steps)
The python code in this repository is used in the SHERPA interface, available at https://jeodpp.jrc.ec.europa.eu/eu/dashboard/voila/render/SHERPA/Sherpa.ipynb.
SHERPA currently uses as input emissions for the year 2019 (from CAMSv6.1, including condensables), EMEP air quality model (version 4.45) to derive the source receptor relationships, and meteorology at 2015. The spatial resolution in the SHERPA interface is 0.1x0.05 degrees.
In particular, the SHERPA interface uses the code of the following modules:
- Module 1 (Scenario assessment): to simulate the impact on air quality of a specific emission reduction scenario
- Module 3 (Source allocation): to understand how the air quality in a given area is influenced by different sources. This module runs in two modes: precursor-based source allocation, and sector-based one. Note that this module works running module 4 and module 1, in sequence.
- Module 4 (Potency): this module computes potencies, and it is not directly launched by the user. On the contrary, it is used by Module 3.
- Module 6 (Governance): to analyze how one should coordinate with the surrounding regions to optimally improve air quality;
- Module 8 (Health impact): to evaluate PM2.5 health-related impact, when running module 1
- Module 9 (Aggregation): to aggregate emissions and concentrations, at NUTS or FUAs level.
- Module 10 (Cost module): to compute costs of end-of-pipe technologies required to reach a given emission reduction target.
The current SHERPA versions works with yearly aggregations of emissions and concentrations, and considers 'ground level' (i.e. traffic) and 'high level' (i.e. point sources) emission sources together. On request, SHERPA SRRs working on seasonal time aggregation (for better time granularity), and splitting 'ground level' and 'high level' sources (for better description of the emission impact on concentrations) are available for the users.
A full list of papers can be found at: https://aqm.jrc.ec.europa.eu/Section/Sherpa/Document.
Below a selection of key papers on SHERPA:
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Bessagnet B, Pisoni E, Thunis P, Mascherpa A. Design and implementation of a new module to evaluate the cost of air pollutant abatement measures. (2022) Journal of Environmental Management;317:115486. https://pubmed.ncbi.nlm.nih.gov/35751296/
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Thunis, P., Clappier, A., De Meij, A., Pisoni, E., Bessagnet, B., & Tarrason, L. Why is the city's responsibility for its air pollution often underestimated? A focus on PM2.5. (2021) Atmospheric Chemistry and Physics, 21(24), 18195-18212. https://acp.copernicus.org/articles/21/18195/2021/
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Degraeuwe, B., Pisoni, E., Thunis, P. Prioritising the sources of pollution in European cities: Do air quality modelling applications provide consistent responses? (2020) Geoscientific Model Development, 13 (11), pp. 5725-5736. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85096928556&doi=10.5194%2fgmd-13-5725-2020&partnerID=40&md5=d4cb7a3413af75830b782af529db3727
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Sartini, L., Antonelli, M., Pisoni, E., Thunis, P. From emissions to source allocation: Synergies and trade-offs between top-down and bottom-up information (2020) Atmospheric Environment: X, 7, art. no. 100088, . https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090038106&doi=10.1016%2fj.aeaoa.2020.100088&partnerID=40&md5=c41e93233b2f100e7dcda69e064a90ea
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Belis, C.A., Pisoni, E., Degraeuwe, B., Peduzzi, E., Thunis, P., Monforti-Ferrario, F., Guizzardi, D. Urban pollution in the Danube and Western Balkans regions: The impact of major PM2.5 sources (2019) Environment International, 133, art. no. 105158, . https://www.scopus.com/inward/record.uri?eid=2-s2.0-85073224266&doi=10.1016%2fj.envint.2019.105158&partnerID=40&md5=81f54f1ee6fc53184059b655797f3ffa
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Pisoni, E., Thunis, P., Clappier, A. Application of the SHERPA source-receptor relationships, based on the EMEP MSC-W model, for the assessment of air quality policy scenarios (2019) Atmospheric Environment: X, 4, art. no. 100047, . https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072582264&doi=10.1016%2fj.aeaoa.2019.100047&partnerID=40&md5=34146ae8e90b2bc98ed4babfea0991a3
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Peduzzi, E., Pisoni, E., Clappier, A., Thunis, P. Multi-level policies for air quality: implications of national and sub-national emission reductions on population exposure (2018) Air Quality, Atmosphere and Health, 11 (9), pp. 1121-1135. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85053388835&doi=10.1007%2fs11869-018-0613-1&partnerID=40&md5=ec09ca92720060078aac98a3408f85a5
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Monforti-Ferrario, F., Kona, A., Peduzzi, E., Pernigotti, D., Pisoni, E. The impact on air quality of energy saving measures in the major cities signatories of the Covenant of Mayors initiative (2018) Environment International, 118, pp. 222-234. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85048115990&doi=10.1016%2fj.envint.2018.06.001&partnerID=40&md5=39ee982bcf359ed03505d2e3d33ec995
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Pisoni, E., Albrecht, D., Mara, T.A., Rosati, R., Tarantola, S., Thunis, P. Application of uncertainty and sensitivity analysis to the air quality SHERPA modelling tool (2018) Atmospheric Environment, 183, pp. 84-93. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85045688029&doi=10.1016%2fj.atmosenv.2018.04.006&partnerID=40&md5=9432074fa33ac072995b6076f64cce3c
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Thunis, P. On the validity of the incremental approach to estimate the impact of cities on air quality (2018) Atmospheric Environment, 173, pp. 210-222. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85034083743&doi=10.1016%2fj.atmosenv.2017.11.012&partnerID=40&md5=f877863da1d86c9bb5d844870ce349aa
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Thunis, P., Degraeuwe, B., Pisoni, E., Ferrari, F., Clappier, A. On the design and assessment of regional air quality plans: The SHERPA approach (2016) Journal of Environmental Management, 183, pp. 952-958. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994012263&doi=10.1016%2fj.jenvman.2016.09.049&partnerID=40&md5=1561546680304fcf57e914bdf441d452