SHERPA

Screening for High Emission Reduction Potential on Air
Fact Sheet

Source: Commission modelling inventory and knowledge management system (MIDAS)

Date of Report Generation: Mon Jan 12 2026

Dissemination: Public

© European Union, 2026

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Overview

Acronym

SHERPA

Full title

Screening for High Emission Reduction Potential on Air

Main purpose

A screening integrated assessment model to design policy scenarios to improve air quality at local, regional and national scales, focusing on source allocation of pollution, impact of abatement strategies on air pollution and governance issues.

Summary

SHERPA is an integrated assessment tool, developed since June 2016 by JRC. It aims at supporting local and regional authorities in preparing air quality plans and assessing their impacts on concentration levels.

The tool is based on simplified emission-concentration relationships that allow for a rapid screening of the impacts of emission reductions plans in any European region, or set of regions.

SHERPA can be used for the formulation, implementation and evaluation of EU policies, legislation and other measures related to air quality. In particular, it can be used in the frame of Impact Assessment procedures to examine potential environmental consequences of perspective regional air quality policies, and evaluate options to improve the effectiveness of the EU action.

Model categories

Climate and air quality

Model keywords

air pollutionemission abatement measuresatmosphereair quality policyintegrated assessment modelling

Model homepage

https://knowledge4policy.ec.europa.eu/fairmode/sherpa_en

Ownership and Licence

Ownership

Third-party ownership (commercial companies, Member States, other organisations, …)

Ownership details

International Institute for Applied Systems Analysis (IIASA)

Licence type

Non-Free Software licence

The license has one or more of the following restrictions: it prohibits creation of derivative works; it prohibits commercial use; it obliges to share the licensed or derivative works on the same conditions.

Details

Structure and approach

SHERPA delivers information on

  1. the potential for abatement strategies in a given region/city/country,
  2. source allocation in terms of either emission precursor (e.g. Primary Particulate, NOx, …) or activity sector (e.g. transport, energy…) and
  3. the key neighboring regions/countries with whom to collaborate to increase the efficiency of air quality plans.

SHERPA currently covers the following pollutants: NO2, PM10 and PM2.5 (yearly averages). For PM2.5, it also computes related health impacts (i.e. premature deaths, Years of Lost Life). Since a full chemistry transport model (CTM) cannot be used online to determine the relationship between emissions and resulting concentrations, simplified relationships are constructed on the basis of pre-elaborated data based on a set of CTM calculations.

Main components:   

  • Source allocation module: calculates the contribution of any emission sector/precursor to the concentration levels at one given location
  • Governance analysis: calculates the contribution of each region in Europe to the concentration observed at one location for a given sector/precursor 
  • Scenario: calculates the impact of a given emission scenario (specified in terms of sector/precursor relative reductions) on concentrations.
  • Atlas module: it allows to reproduce the results as presented in the "Urban PM2.5 Air Quality in European Cities", in particular the source of pollution for 150 main cities in Europe.

Input and parametrization

SHERPA is configured and released with a default dataset covering all Europe. It can however be fed by user data for the same European domain or for a smaller domain, provided the following input data are made available:

  • gridded emission inventory,
  • emission-concentration relationships (based on a full air quality model)
  • shape files defining the areas of interest where emission reductions might take place. 

SHERPA is based on the assumption that emissions and concentrations are linearly related, an assumption that has been shown to be valid for yearly averaged concentrations. SHERPA is therefore limited to the analysis of impacts for yearly concentrations.

The emission-concentrations relationships can be obtained through the use of a CTM (Chemistry transport model) such as CHIMERE.

Main output

SHERPA provides:

  • concentration levels that would result from any given emission abatement scenario.
  • information in terms of source contributions, either from sectors or from regional entities.
  • Information on the optimal level of coordination among local authorities needed to improve air quality.

 

Spatial & Temporal extent

The output has the following spatial-temporal resolution and extent:

Spatial & Temporal extent for the output
ParameterDescription
Spatial extent / country coverage
EU28
Spatial resolution
Roughly 7 km as default, but any resolution can be adapted with appropriate input data
Temporal extent
SHERPA works on relative emission reductions applied on a base case simulations. Temporal extent is therefore not applicable
Temporal resolution
Yearly

Quality & Transparency

Quality

Model uncertainties

Models are by definition affected by uncertainties (in input data, input parameters, scenario definitions, etc.). Have the model uncertainties been quantified? Are uncertainties accounted for in your simulations?

yes
Uncertainty and sensitivity analysis have been performed with the help of the Competence Centre on Modelling.

    Sensitivity analysis

    Sensitivity analysis helps identifying the uncertain inputs mostly responsible for the uncertainty in the model responses. Has the model undergone sensitivity analysis?

    yes
    Yes, in cooperation with the Competence Centre on Modelling (JRC.I1).

      Have model results been published in peer-reviewed articles?

      no
      We have scientific papers on the SHERPA methodology, but no external peer review.

        Has the model formally undergone scientific review by a panel of international experts?

        Please note that this does not refer to the cases when model results were validated by stakeholders.

        not provided

          Model validation

          Has model validation been done? Have model predictions been confronted with observed data (ex-post)?

          yes
          Yes, for the source-receptor model basecase concentrations.

            Transparency

            To what extent do input data come from publicly available sources?

            This may include sources accessible upon subscription and/or payment

            not provided

            Is the full model database as such available to external users?

            Whether or not it implies a specific procedure or a fee

            yes
            When downloading the tool all the input data are available. Under the following website:

              Have model results been presented in publicly available reports?

              Note this excludes IA reports.

              not provided

              Have output datasets been made publicly available?

              Note this could also imply a specific procedure or a fee.

              yes

              Is there any user friendly interface presenting model results that is accessible to the public?

              For instance: Dashboard, interactive interfaces...

              not provided

                Has the model been documented in a publicly available dedicated report or a manual?

                Note this excludes IA reports.

                yes
                Everything is well detailed in 3 publications on scientific literature. SHERPA documentation: available as a ‘help’ when installing the tool.

                Is the model code open-source?

                no

                Can the code be accessed upon request?

                yes

                The model’s policy relevance and intended role in the policy cycle

                The model is designed to contribute to the following policy areas

                • Climate action
                • Energy
                • Environment
                • Institutional affairs
                • Transport

                The model is designed to contribute to the following phases of the policy cycle

                • Evaluation – such as ex-post evaluation
                • Formulation – such as ex-ante Impact Assessments
                • Implementation – this also includes monitoring

                The model’s potential

                SHERPA can be used for the formulation, implementation and evaluation of EU policies, legislation and other measures related to air quality. In particular, it can be used in the frame of Impact Assessment procedures to examine potential environmental consequences of perspective regional air quality policies, and evaluate options to improve the effectiveness of the EU action. In this context, a key partner is DG-ENV.

                SHERPA has also been used in the frame of the "Partnership on Air Quality", coordinated by DG-REGIO.

                Previous use of the model in ex-ante impact assessments of the European Commission

                Use of the model in ex-ante impact assessments since July 2017.

                Bibliographic references

                Studies that uses the model or its results

                Evaluating the impact of “Sustainable Urban Mobility Plans” on urban background air quality  

                Published in 2018
                Pisoni, E., Christidis, P., Thunis, P. and Trombetti, M., Evaluating the impact of “Sustainable Urban Mobility Plans” on urban background air quality , JOURNAL OF ENVIRONMENTAL MANAGEMENT, ISSN 0301-4797 (online), 231, 2019, p. 249-255, JRC112933.

                PM2.5 source allocation in European cities: a SHERPA modelling study 

                Published in 2018
                Thunis, P., Degraeuwe, B., Pisoni, E., Trombetti, M., Peduzzi, E., Belis, C., Wilson, J., Clappier, A. and Vignati, E., PM2.5 source allocation in European cities: a SHERPA modelling study, ATMOSPHERIC ENVIRONMENT, 2018, ISSN 0004-6981 (online), 187, p. 93-106, JRC111082.

                The impact on air quality of energy saving measures in the major cities signatories of the Covenant of Mayors initiative  

                Published in 2018
                Monforti-Ferrario, F., Kona, A., Peduzzi, E., Pernigotti, D. and Pisoni, E., The impact on air quality of energy saving measures in the major cities signatories of the Covenant of Mayors initiative , ENVIRONMENT INTERNATIONAL, ISSN 0160-4120, 118, 2018, p. 222-234, JRC107670.

                Urban PM2.5 Atlas: Air Quality in European cities 

                Published in 2017
                Thunis, P., Degraeuwe, B., Peduzzi, E., Pisoni, E., Trombetti, M., Vignati, E., Wilson, J., Belis, C. and Pernigotti, D., Urban PM2.5 Atlas: Air Quality in European cities, EUR 28804 EN, Publications Office of the European Union, Luxembourg, 2017, ISBN 978-92-79-73876-0 (online),978-92-79-73875-3 (print),978-92-79-75274-2 (ePub), doi:10.2760/336669 (online),10.2760/851626 (print),10.2760/865663 (ePub), JRC108595.

                European cities: territorial analysis of characteristics and trends - An application of the LUISA Modelling Platform (EU Reference Scenario 2013 - Updated Configuration 2014) 

                Published in 2015
                Kompil M, Aurambout J, Ribeiro Barranco R, Barbosa A, Jacobs C, Pisoni E, Zulian G, Vandecasteele I, Trombetti M, Vizcaino M, Vallecillo Rodriguez S, Batista E Silva F, Baranzelli C, Mari Rivero I, Perpiña Castillo C, Polce C, Maes J, Lavalle C. European cities: territorial analysis of characteristics and trends - An application of the LUISA Modelling Platform (EU Reference Scenario 2013 - Updated Configuration 2014). EUR 27709. Luxembourg (Luxembourg): Publications Office of the European Union; 2015. JRC100001

                Peer review for model validation

                Spatial and sector-specific contributions of emissions to ambient air pollution and mortality in European cities: a health impact assessment 

                Published in 2023
                Khomenko, S., Pisoni, E., Thunis, P., Bessagnet, B., Cirach, M., Iungman, T., Barboza, E. P., Khreis, H., Mueller, N., Tonne, C., de Hoogh, K., Hoek, G., Chowdhury, S., Lelieveld, J., & Nieuwenhuijsen, M. (2023). Spatial and sector-specific contributions of emissions to ambient air pollution and mortality in European cities: a health impact assessment. The Lancet Public Health, 8(7), e546–e558. https://doi.org/10.1016/s2468-2667(23)00106-8

                Design and implementation of a new module to evaluate the cost of air pollutant abatement measures 

                Published in 2022
                Bessagnet, B., Pisoni, E., Thunis, P., & Mascherpa, A. (2022). Design and implementation of a new module to evaluate the cost of air pollutant abatement measures. Journal of Environmental Management, 317, 115486. https://doi.org/10.1016/j.jenvman.2022.115486

                Prioritising the sources of pollution in European cities: do air quality modelling applications provide consistent responses? 

                Published in 2020
                Degraeuwe, B., Pisoni, E., & Thunis, P. (2020). Prioritising the sources of pollution in European cities: do air quality modelling applications provide consistent responses? Geoscientific Model Development, 13(11), 5725–5736. https://doi.org/10.5194/gmd-13-5725-2020

                From emissions to source allocation: Synergies and trade-offs between top-down and bottom-up information 

                Published in 2020
                Sartini, L., Antonelli, M., Pisoni, E., & Thunis, P. (2020). From emissions to source allocation: Synergies and trade-offs between top-down and bottom-up information. Atmospheric Environment: X, 7, 100088. https://doi.org/10.1016/j.aeaoa.2020.100088

                Application of the SHERPA source-receptor relationships, based on the EMEP MSC-W model, for the assessment of air quality policy scenarios 

                Published in 2019
                Pisoni, E., Thunis, P., & Clappier, A. (2019). Application of the SHERPA source-receptor relationships, based on the EMEP MSC-W model, for the assessment of air quality policy scenarios. Atmospheric Environment: X, 4, 100047. https://doi.org/10.1016/j.aeaoa.2019.100047

                Application of uncertainty and sensitivity analysis to the air quality SHERPA modelling tool 

                Published in 2018
                Pisoni, E., Albrecht, D., Mara, T., Rosati, R., Tarantola, S. and Thunis, P., Application of uncertainty and sensitivity analysis to the air quality SHERPA modelling tool, ATMOSPHERIC ENVIRONMENT, ISSN 1352-2310, 183, 2018, p. 84-93, JRC103211.

                Adding spatial flexibility to source-receptor relationships for air quality modeling 

                Published in 2017
                Pisoni E; Clappier A; Degraeuwe B; Thunis P. Adding spatial flexibility to source-receptor relationships for air quality modeling. ENVIRONMENTAL MODELLING and SOFTWARE 90; 2017. p. 68-77. JRC102815

                On the design and assessment of regional air quality plans: the SHERPA approach 

                Published in 2016
                Thunis P; Degraeuwe B; Pisoni E; Ferrari F; Clappier A. On the design and assessment of regional air quality plans: the SHERPA approach. JOURNAL OF ENVIRONMENTAL MANAGEMENT 183 - Part 3; 2016. p. 952-958. JRC102824

                A new approach to design source–receptor relationships for air quality modelling 

                Published in 2015
                Clappier, A., Pisoni, E., & Thunis, P. (2015). A new approach to design source–receptor relationships for air quality modelling. Environmental Modelling & Software, 74, 66–74. doi:10.1016/j.envsoft.2015.09.007

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