PyCHAM (v2.1.1): a Python box model for simulating aerosol chambers

O’Meara, Simon; Xu, Shuxuan; Topping, David; Alfarra, M. R.; Capes, Gerard; Lowe, Douglas; Shao, Yunqi; McFiggans, G.

doi:10.5194/gmd-14-675-2021

Cited by 10 publications

(3 citation statements)

References 54 publications

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“…In addition, the box model is also a powerful tool in environmental chamber studies (Chen et al, 2015;Novelli et al, 2018). Several box models have been developed and applied in previous studies, such as AtChem (Sommariva et al, 2020), Chemistry As A Box Model Application (CAABA/MECCA) (Sander et al, 2011(Sander et al, , 2019, Framework for 0-D Atmospheric Modeling (F0AM) (Wolfe et al, 2016), PyCHAM (O'Meara et al, 2021), JlBox (Huang and Topping, 2021) and Photochemical Box Model incorporating the Master Chemical Mechanism (PBM-MCM) (Wang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Rapid Adaptive Optimization Model for Atmospheric Chemistry (ROMAC) v1.0

Li,

Zhang,

Zhao

et al. 2023

Geosci. Model Dev.

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Abstract. The Rapid Adaptive Optimization Model for Atmospheric Chemistry (ROMAC) is a flexible and computationally efficient photochemical box model. Its unique adaptive dynamic optimization module allows for the dynamic and rapid estimation of the impact of chemical and physical processes on pollutant concentration. ROMAC outperforms traditional box models in evaluating the influence of physical processes on pollutant concentrations. Its ability to quantify the effects of chemical and physical processes on pollutant concentrations has been confirmed through chamber and field observation cases. Since the development of a variable-step and variable-order numerical solver that eliminates the need for Jacobian matrix processing, the computational efficiency of ROMAC has seen a marked improvement with only a marginal increase in error. Specifically, the computational efficiency has improved by 96 % when compared to several established box models, such as F0AM and AtChem. Moreover, the solver maintains a discrepancy of less than 0.1 % when its results are compared with those obtained from a high-precision solver in AtChem.

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Section: Introductionmentioning

confidence: 99%

Rapid Adaptive Optimization Model for Atmospheric Chemistry (ROMAC) v1.0

Li,

Zhang,

Zhao

et al. 2023

Geosci. Model Dev.

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“…Recently, Python-based solutions have become available, e.g. PyCham (O'Meara et al, 2021) and PyBox (Topping et al, 2018). Yet, it can be said that in terms of usability, the scientific tools are not on par with current-generation software in general.…”

Section: Introductionmentioning

confidence: 99%

Atmospherically Relevant Chemistry and Aerosol box model – ARCA box (version 1.2)

et al. 2022

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Abstract. We introduce the Atmospherically Relevant Chemistry and Aerosol box model ARCA box (v.1.2.2). It is a zero-dimensional process model with a focus on atmospheric chemistry and submicron aerosol processes, including cluster formation. A novel feature in the model is its comprehensive graphical user interface, allowing for detailed configuration and documentation of the simulation settings, flexible model input, and output visualization. Additionally, the graphical interface contains tools for module customization and input data acquisition. These properties – customizability, ease of implementation and repeatability – make ARCA an invaluable tool for any atmospheric scientist who needs a view on the complex atmospheric aerosol processes. ARCA is based on previous models (MALTE-BOX, ADiC and ADCHEM), but the code has been fully rewritten and reviewed. The gas-phase chemistry module incorporates the Master Chemical Mechanism (MCMv3.3.1) and Peroxy Radical Autoxidation Mechanism (PRAM) but can use any compatible chemistry scheme. ARCA's aerosol module couples the ACDC (Atmospheric Cluster Dynamics Code) in its particle formation module, and the discrete particle size representation includes the fully stationary and fixed-grid moving average methods. ARCA calculates the gas-particle partitioning of low-volatility organic vapours for any number of compounds included in the chemistry, as well as the Brownian coagulation of the particles. The model has parametrizations for vapour and particle wall losses but accepts user-supplied time- and size-resolved input. ARCA is written in Fortran and Python (user interface and supplementary tools), can be installed on any of the three major operating systems and is licensed under GPLv3.

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“…PyBox is the basis for PyCHAM, a Python box model for simulating aerosol chambers (O'Meara et al, 2021) and for JlBox (Huang and Topping, 2020), a high-performance community multi-phase atmospheric 0D box-model, written in Julia. JlBox simulates the chemical kinetics of a gas phase and a fully coupled gas-particle model with dynamic partitioning to a fully moving sectional size distribution.…”

Section: Introductionmentioning

confidence: 99%

Chemistry Across Multiple Phases (CAMP) version 1.0: An integrated multi-phase chemistry model

Dawson¹,

Guzmán²,

Curtis³

et al. 2021

Preprint

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A flexible treatment for gas-and aerosol-phase chemical processes has been developed for models of diverse scale, from box models up to global models. At the core of this novel framework is an "abstracted aerosol representation" that allows a given chemical mechanism to be solved in atmospheric models with different aerosol representations (e.g., sectional, modal, or particle-resolved). This is accomplished by treating aerosols as a collection of condensed phases that are implemented according to the aerosol representation of the host model. The framework also allows multiple chemical processes (e.g., gas-and aerosol-phase chemical reactions, emissions, deposition, photolysis, and mass-transfer) to be solved simultaneously as a single system. The flexibility of the model is achieved by (1) using an object-oriented design that facilitates extensibility to new types of chemical processes and to new ways of representing aerosol systems;(2) runtime model configuration using JSON input files that permits making changes to any part of the chemical mechanism without recompiling the model; this widely used, humanreadable format allows entire gas-and aerosol-phase chemical mechanisms to be described with as much complexity as necessary; and (3) automated comprehensive testing that ensures stability of the code as new functionality is introduced. Together, these design choices enable users to build a customized multiphase mechanism, without having to handle pre-processors, solvers or compilers. Removing these hurdles makes this type of modeling accessible to a much wider community, including modelers, experimentalists, and educators. This new treatment compiles as a stand-alone library and has been deployed in the particle-resolved PartMC model and in the MONARCH chemical weather prediction system for use at regional and global scales. Results from the initial deployment to box models of different complexity and MONARCH will be discussed, along with future extension to more complex gas-aerosol systems, and the integration of GPU-based solvers.

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PyCHAM (v2.1.1): a Python box model for simulating aerosol chambers

Cited by 10 publications

References 54 publications

Rapid Adaptive Optimization Model for Atmospheric Chemistry (ROMAC) v1.0

Rapid Adaptive Optimization Model for Atmospheric Chemistry (ROMAC) v1.0

Atmospherically Relevant Chemistry and Aerosol box model – ARCA box (version 1.2)

Chemistry Across Multiple Phases (CAMP) version 1.0: An integrated multi-phase chemistry model

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