PyCHAM: CHemistry with Aerosol Microphysics in Python

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

doi:10.21105/joss.01918

Cited by 5 publications

(2 citation statements)

References 16 publications

(16 reference statements)

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“…[18][19][20] As a result, more packages designed for chemistry can be found. [21][22][23][24][25][26] The implementation for this publication was done using Jupyter notebooks.…”

Section: Pythonmentioning

confidence: 99%

“…[18][19][20] As a result, more packages designed for chemistry can be found. [21][22][23][24][25][26] The implementation for this publication was done using Jupyter notebooks. Those give the possibility to mix Python code, markdown text, and LaTeX code, which makes it easy to comment on the code and makes it readable and accessible for people new to programming.…”

Section: Pythonmentioning

confidence: 99%

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Prediction of suitable catalysts for the OCM reaction by combining an evolutionary approach and machine learning

Meyenn

2023

Energy Adv.

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“…[18][19][20] As a result, more packages designed for chemistry can be found. [21][22][23][24][25][26] The implementation for this publication was done using Jupyter notebooks.…”

Section: Pythonmentioning

confidence: 99%

Section: Pythonmentioning

confidence: 99%

Prediction of suitable catalysts for the OCM reaction by combining an evolutionary approach and machine learning

Meyenn

2023

Energy Adv.

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INCHEM-Py: An open source Python box model for indoor air chemistry

Shaw¹,

Carslaw²

2021

JOSS

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

O’Meara

Topping

et al. 2021

Geosci. Model Dev.

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Abstract. In this paper the CHemistry with Aerosol Microphysics in Python (PyCHAM) box model software for aerosol chambers is described and assessed against benchmark simulations for accuracy. The model solves the coupled system of ordinary differential equations for gas-phase chemistry, gas–particle partitioning and gas–wall partitioning. Additionally, it can solve for coagulation, nucleation and particle loss to walls. PyCHAM is open-source, whilst the graphical user interface, modular structure, manual, example plotting scripts, and suite of tests for troubleshooting and tracking the effect of modifications to individual modules have been designed for optimal usability. In this paper, the modelled processes are individually assessed against benchmark simulations, and key parameters are described. Examples of output when processes are coupled are also provided. Sensitivity of individual processes to relevant parameters is illustrated along with convergence of model output with increasing temporal resolution and number of size bins. The latter sensitivity analysis informs our recommendations for model setup. Where appropriate, parameterisations for specific processes have been chosen for their general applicability, with their rationale detailed here. It is intended for PyCHAM to aid the design and analysis of aerosol chamber experiments, with comparison of simulations against observations allowing improvement of process understanding that can be transferred to ambient atmosphere simulations.

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PyCHAM: CHemistry with Aerosol Microphysics in Python

Cited by 5 publications

References 16 publications

Prediction of suitable catalysts for the OCM reaction by combining an evolutionary approach and machine learning

Prediction of suitable catalysts for the OCM reaction by combining an evolutionary approach and machine learning

INCHEM-Py: An open source Python box model for indoor air chemistry

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

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