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

2021 ◽  
Vol 14 (2) ◽  
pp. 675-702
Author(s):  
Simon Patrick O'Meara ◽  
Shuxuan Xu ◽  
David Topping ◽  
M. Rami Alfarra ◽  
Gerard Capes ◽  
...  
Keyword(s):  
Gas Phase ◽  
Coupled System ◽  
Box Model ◽  
Ambient Atmosphere ◽  
General Applicability ◽  
Gas Phase Chemistry ◽  
Process Understanding ◽  
Aerosol Chamber ◽  
Model Setup ◽  
Phase Chemistry

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.

scholarly journals PyCHAM (v1.3.4): a Python box model for simulating aerosol chambers

2020 ◽  
Author(s):  
Simon Patrick O'Meara ◽  
Shuxuan Xu ◽  
David Topping ◽  
M. Rami Alfarra ◽  
Gerard Capes ◽  
...  
Keyword(s):  
Coupled System ◽  
Box Model ◽  
Ambient Atmosphere ◽  
General Applicability ◽  
Gas Phase Chemistry ◽  
Process Understanding ◽  
Aerosol Chamber ◽  
Temporal And Spatial ◽  
Model Setup ◽  
Phase Chemistry

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 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.

scholarly journals Simulation of the diurnal variations of the oxygen isotope anomaly (Δ<sup>17</sup>O) of reactive atmospheric species

2011 ◽  
Vol 11 (8) ◽  
pp. 3653-3671 ◽  
Author(s):  
S. Morin ◽  
R. Sander ◽  
J. Savarino
Keyword(s):  
Gas Phase ◽  
Atmospheric Chemistry ◽  
Isotopic Signature ◽  
Diurnal Variations ◽  
Box Model ◽  
Computational Costs ◽  
Atmospheric Species ◽  
Gas Phase Chemistry ◽  
Atmospheric Nitrate ◽  
Phase Chemistry

Abstract. The isotope anomaly (Δ17O) of secondary atmospheric species such as nitrate (NO3−) or hydrogen peroxide (H2O2) has potential to provide useful constrains on their formation pathways. Indeed, the Δ17O of their precursors (NOx, HOx etc.) differs and depends on their interactions with ozone, which is the main source of non-zero Δ17O in the atmosphere. Interpreting variations of Δ17O in secondary species requires an in-depth understanding of the Δ17O of their precursors taking into account non-linear chemical regimes operating under various environmental settings. This article reviews and illustrates a series of basic concepts relevant to the propagation of the Δ17O of ozone to other reactive or secondary atmospheric species within a photochemical box model. We present results from numerical simulations carried out using the atmospheric chemistry box model CAABA/MECCA to explicitly compute the diurnal variations of the isotope anomaly of short-lived species such as NOx and HOx. Using a simplified but realistic tropospheric gas-phase chemistry mechanism, Δ17O was propagated from ozone to other species (NO, NO2, OH, HO2, RO2, NO3, N2O5, HONO, HNO3, HNO4, H2O2) according to the mass-balance equations, through the implementation of various sets of hypotheses pertaining to the transfer of Δ17O during chemical reactions. The model results confirm that diurnal variations in Δ17O of NOx predicted by the photochemical steady-state relationship during the day match those from the explicit treatment, but not at night. Indeed, the Δ17O of NOx is "frozen" at night as soon as the photolytical lifetime of NOx drops below ca. 10 min. We introduce and quantify the diurnally-integrated isotopic signature (DIIS) of sources of atmospheric nitrate and H2O2, which is of particular relevance to larger-scale simulations of Δ17O where high computational costs cannot be afforded.

scholarly journals Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10

2019 ◽  
Vol 12 (3) ◽  
pp. 1209-1225 ◽  
Author(s):  
Christoph A. Keller ◽  
Mat J. Evans
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Gas Phase ◽  
Atmospheric Chemistry ◽  
Random Forest Regression ◽  
Data Set ◽  
Gas Phase Chemistry ◽  
Chemical Conditions ◽  
Phase Chemistry ◽  
The Impact

Abstract. Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models are numerically intense, and previous attempts to reduce the numerical cost of chemistry solvers have not delivered transformative change. We show here the potential of a machine learning (in this case random forest regression) replacement for the gas-phase chemistry in atmospheric chemistry transport models. Our training data consist of 1 month (July 2013) of output of chemical conditions together with the model physical state, produced from the GEOS-Chem chemistry model v10. From this data set we train random forest regression models to predict the concentration of each transported species after the integrator, based on the physical and chemical conditions before the integrator. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for long-lived species and the absolute concentration for short-lived species. We also find improvements from a simple implementation of chemical families (NOx = NO + NO2). We then implement the trained random forest predictors back into GEOS-Chem to replace the numerical integrator. The machine-learning-driven GEOS-Chem model compares well to the standard simulation. For ozone (O3), errors from using the random forests (compared to the reference simulation) grow slowly and after 5 days the normalized mean bias (NMB), root mean square error (RMSE) and R2 are 4.2 %, 35 % and 0.9, respectively; after 30 days the errors increase to 13 %, 67 % and 0.75, respectively. The biases become largest in remote areas such as the tropical Pacific where errors in the chemistry can accumulate with little balancing influence from emissions or deposition. Over polluted regions the model error is less than 10 % and has significant fidelity in following the time series of the full model. Modelled NOx shows similar features, with the most significant errors occurring in remote locations far from recent emissions. For other species such as inorganic bromine species and short-lived nitrogen species, errors become large, with NMB, RMSE and R2 reaching >2100 % >400 % and <0.1, respectively. This proof-of-concept implementation takes 1.8 times more time than the direct integration of the differential equations, but optimization and software engineering should allow substantial increases in speed. We discuss potential improvements in the implementation, some of its advantages from both a software and hardware perspective, its limitations, and its applicability to operational air quality activities.

Gas Phase Chemistry in Cellulose Fast Pyrolysis

2009 ◽  
Vol 48 (3) ◽  
pp. 1391-1399 ◽  
Author(s):  
R. Lanza ◽  
D. Dalle Nogare ◽  
P. Canu
Keyword(s):  
Gas Phase ◽  
Fast Pyrolysis ◽  
Gas Phase Chemistry ◽  
Phase Chemistry

Mass Spectrometry and Gas-Phase Chemistry of Anilines

ChemInform ◽  
2007 ◽  
Vol 38 (30) ◽  
Author(s):  
Marcos N. Eberlin ◽  
Daniella Vasconcellos Augusti ◽  
Rodinei Augusti
Keyword(s):  
Mass Spectrometry ◽  
Gas Phase ◽  
Gas Phase Chemistry ◽  
Phase Chemistry

Kinetic Analysis of C4 Alkane and Alkene Pyrolysis: Implications for SOFC Operation

2010 ◽  
Vol 7 (4) ◽  
Author(s):  
Ahmed Al Shoaibi ◽  
Anthony M. Dean
Keyword(s):  
Gas Phase ◽  
Solid Oxide ◽  
Oxide Fuel ◽  
Pyrolysis Kinetics ◽  
Fuel Conversion ◽  
Substantial Impact ◽  
Gas Phase Chemistry ◽  
Gas Phase Kinetics ◽  
Hydrocarbon Gas ◽  
Phase Chemistry

Pyrolysis experiments of isobutane, isobutylene, and 1-butene were performed over a temperature range of 550–750°C and a pressure of ∼0.8 atm. The residence time was ∼5 s. The fuel conversion and product selectivity were analyzed at these temperatures. The pyrolysis experiments were performed to simulate the gas-phase chemistry that occurs in the anode channel of a solid-oxide fuel cell (SOFC). The experimental results confirm that molecular structure has a substantial impact on pyrolysis kinetics. The experimental data show considerable amounts of C5 and higher species (∼2.8 mole % with isobutane at 750°C, ∼7.5 mole % with isobutylene at 737.5°C, and ∼7.4 mole % with 1-butene at 700°C). The C5+ species are likely deposit precursors. The results confirm that hydrocarbon gas-phase kinetics have substantial impact on a SOFC operation.

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