FASST(R) – FAst Scenario Screening Tool

FASST(R) is a source receptor model, an R version of the reduced-form TM5- FASST [5] model developed at JRC-Ispra, to compute the annual concentrations of several pollutants p, namely SO2 , NOx , fine Particulate Matter (PM2.5 ) and O3.
The FASST(R) model produces concentrations on a world spatial grid of resolution of 1 ◦ × 1 ◦ , and the fine PM 2.5 concentrations include Particulate Organic Matter (POM), secondary inorganic PM, dust and sea-salt.

The FASST  and FASST(R) model has already been previously used in other studies to assess premature death from air pollution exposure [4,6]. It includes an urban increment algorithm in order to account for the population distribution and the distribution of PM concentrations [3].

The FASST model full validation against TM5 can be found in [5] and more informally in [3]. According to these studies, FASST performs well in comparison with the full chemical transport model TM5 and its reduced form does not compromise the output validity.
The macro-regions emissions are pre-processed in order to obtain world gridded emissions using the Global Energy Assessment [1] as a proxy to disaggregate emissions spatially, as in [3].

FASST(R) was designed to be directly plugged to the WITCH output in order to sequentially calculate the air quality outcomes of a given scenario. It calculates concentrations, premature death from exposure to Ozone and PM 2.5, and crop loss due to Ozone.

Official website: FAst Scenario Screening Tool

[1] GEA. Global energy assessment: Toward a sustainable future, 2017.
[2] M. Krol, S. Houweling, B. Bregman, M. van den Broek, A. Segers, P. van Velthoven, W. Peters, F. Dentener, and P. Bergamaschi. The two-way nested global chemistry transport zoom model tm5: algorithm and applications. Atmospheric Chemistry and Physics, 5(2):417{432, 2005.
[3] J Leitao, R. Van Dingenen, and S. Rao. Report on spatial emissions downscaling and concentrations for health impacts assessment. Technical Report 109.2015, IIASA, Milan, 2013.
[4] Shilpa Rao, Zbigniew Klimont, Joana Leitao, Keywan Riahi, Rita van Dingenen, Lara Aleluia Reis, Katherine Calvin, Frank Dentener, Laurent Drouet, Shinichiro Fuji-mori, Mathijs Harmsen, Gunnar Luderer, Chris Heyes, Jessica Streer, Massimo Tavoni, and Detlef P van Vuuren. A multi-model assessment of the co-bene_ts of climate mitigation for global air quality. Environmental Research Letters, 11(12):124013, 2016.
[5] R. Van Dingenen, F. Dentener, M. Crippa, J. Leitao, E. Marmer, S. Rao, E. Solazzo, and L. Valentini. Tm5-fasst: a global atmospheric source-receptor model for rapid impact analysis of emission changes on air quality and short-lived climate pollutants. Atmo- spheric Chemistry and Physics Discussions, 2018:1{55, 2018.
[6] Aleluia Reis, L.; Drouet, L.; Van Dingenen, R.; Emmerling, J. Future Global Air Quality Indices under Different Socioeconomic and Climate Assumptions. Sustainability 2018, 10, 3645.

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