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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. [ABSTRACT FROM AUTHOR]

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 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 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 and spatial resolution. 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 that PyCHAM aids 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. [ABSTRACT FROM AUTHOR]

Characteristic organic aerosol (OA) emission ratios (ERs) and normalized excess mixing ratios (NEMRs) for biomass burning (BB) events have been calculated from ambient measurements recorded during four field campaigns. Normalized OA mass concentrations measured using Aerodyne Research Inc. quadrupole aerosol mass spectrometers (Q-AMS) reveal a systematic variation in average values between different geographical regions. For each region, a consistent, characteristic ratio is seemingly established when measurements are collated from plumes of all ages and origins. However, there is evidence of strong regional and local-scale variability between separate measurement periods throughout the tropical, subtropical, and boreal environments studied. ERs close to source typically exceed NEMRs in the far-field, despite apparent compositional change and increasing oxidation with age. The absence of any significant downwind mass enhancement suggests no regional net source of secondary organic aerosol (SOA) from atmospheric aging of BB sources, in contrast with the substantial levels of net SOA formation associated with urban sources. A consistent trend of moderately reduced ΔOA/ΔCO ratios with aging indicates a small net loss of OA, likely as a result of the evaporation of organic material from initial fire emissions. Variability in ERs close to source is shown to substantially exceed the magnitude of any changes between fresh and aged OA, emphasizing the importance of fuel and combustion conditions in determining OA loadings from biomass burning. [ABSTRACT FROM AUTHOR]