Modelling aerosol molecular markers in a 3D air quality model: Focus on anthropogenic organic markers

Lanzafame, Grazia Maria; Bessagnet, Bertrand; Srivastava, Deepchandra; Jaffrezo, Jeań Luc; Favez, Olivier; Albinet, Alexandre; Couvidat, Florian

doi:10.1016/j.scitotenv.2022.155360

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Input Files Of Atmospheric Conditions1

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2022

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Cited by 2 publications

(1 citation statement)

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“…That information is stored in one directory (read by "pathInitFiles" in the configuration file). The given input files are extracted from CHIMERE simulations [3].…”

Section: Input Files Of Atmospheric Conditionsmentioning

confidence: 99%

Supplementary material to "GENerator of reduced Organic Aerosol mechanism (GENOA v1.0): An automatic generation tool of semi-explicit mechanisms"

Wang¹,

Couvidat²,

Sartelet³

2022

Preprint

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“…That information is stored in one directory (read by "pathInitFiles" in the configuration file). The given input files are extracted from CHIMERE simulations [3].…”

Section: Input Files Of Atmospheric Conditionsmentioning

confidence: 99%

Supplementary material to "GENerator of reduced Organic Aerosol mechanism (GENOA v1.0): An automatic generation tool of semi-explicit mechanisms"

Wang¹,

Couvidat²,

Sartelet³

2022

Preprint

View full text Add to dashboard Cite

GENerator of reduced Organic Aerosol mechanism (GENOA v1.0): an automatic generation tool of semi-explicit mechanisms

2022

Self Cite

View full text Add to dashboard Cite

Abstract. This paper describes the GENerator of reduced Organic Aerosol mechanism (GENOA) that produces semi-explicit mechanisms for simulating the formation and evolution of secondary organic aerosol (SOA) in air quality models. Using a series of predefined reduction strategies and evaluation criteria, GENOA trains and reduces SOA mechanisms from near-explicit chemical mechanisms (e.g., the Master Chemical Mechanism – MCM) under representative atmospheric conditions. As a consequence, these trained SOA mechanisms can preserve the accuracy of detailed gas-phase chemical mechanisms on SOA formation (e.g., molecular structures of crucial organic compounds, the effect of “non-ideality”, and the hydrophilic/hydrophobic partitioning of aerosols), with a size (in terms of reaction and species numbers) that is manageable for three-dimensional (3-D) aerosol modeling (e.g., regional chemical transport models). Applied to the degradation of sesquiterpenes (as β-caryophyllene) from MCM, GENOA builds a concise SOA mechanism (2 % of the MCM size) that consists of 23 reactions and 15 species, with 6 of them being condensable. The generated SOA mechanism has been evaluated regarding its ability to reproduce SOA concentrations under the varying atmospheric conditions encountered over Europe, with an average error lower than 3 %.

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Modelling aerosol molecular markers in a 3D air quality model: Focus on anthropogenic organic markers

Cited by 2 publications

References 105 publications

Supplementary material to "GENerator of reduced Organic Aerosol mechanism (GENOA v1.0): An automatic generation tool of semi-explicit mechanisms"

Supplementary material to "GENerator of reduced Organic Aerosol mechanism (GENOA v1.0): An automatic generation tool of semi-explicit mechanisms"

GENerator of reduced Organic Aerosol mechanism (GENOA v1.0): an automatic generation tool of semi-explicit mechanisms

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