AtChem (version 1), an open-source box model for  the Master Chemical Mechanism

Sommariva, Roberto; Cox, Sam; Martin, C.; Borońska, Kasia; Young, J. C.; Jimack, Peter K.; Pilling, Michael J.; Matthaios, Vasileios N.; Nelson, Beth S.; Newland, Mike J.; Panagi, Marios; Bloss, William J.; Monks, P. S.; Rickard, Andrew R.

doi:10.5194/gmd-13-169-2020

Cited by 54 publications

(46 citation statements)

References 55 publications

(68 reference statements)

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“…A series of box model simulations was conducted to clarify the mechanism of NC formation during the second period of the experimental campaign (see below), when secondary chemistry dominated the formation of NACs. Simulations were performed using AtChem (Sommariva et al, 2020), an online zero-dimensional box model, together with chemical reactions extracted from the Master Chemical Mechanism (MCMv3.3.1) via the website http://mcm.leeds.ac.uk/MCM (Jenkin et al, 2003;Saunders 10 et al, 2003). AtChem was previously utilized to simulate the formation of formic acid and nitrophenol at a site dominated by oil and gas production (Yuan et al, 2016).…”

Section: Box Model Simulation Of Nitrocatechol Using the Atchem Toolmentioning

confidence: 99%

Ambient Nitro-Aromatic Compounds – Biomass Burning versus Secondary Formation in rural China

Salvador¹,

Tang²,

Priestley³

et al. 2020

Preprint

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Abstract. Nitro-aromatic compounds (NACs) were measured hourly at a rural site in China during wintertime to monitor the changes due to local and regional impacts of biomass burning (BB). Concurrent and continuous measurements of the concentrations of 16 NACs in the gas and particle phases were performed with a time-of-flight chemical ionization mass spectrometer (CIMS) equipped with a Filter Inlet for Gas and Aerosol (FIGAERO) unit using iodide as the reagent ion. NACs accounted for

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Section: Box Model Simulation Of Nitrocatechol Using the Atchem Toolmentioning

confidence: 99%

Ambient Nitro-Aromatic Compounds – Biomass Burning versus Secondary Formation in rural China

Salvador¹,

Tang²,

Priestley³

et al. 2020

Preprint

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“…Mechanism (MCM v3.3.1;Jenkin et al, 2015) into the AtChem2 modelling toolkit (Sommariva et al, 2020), was used to identify the main drivers of in situ O3 production, P(O3), in Delhi.…”

Section: Model Descriptionmentioning

confidence: 99%

In situ Ozone Production is highly sensitive to Volatile Organic Compounds in the Indian Megacity of Delhi

Nelson

Stewart

Drysdale

et al. 2021

Preprint

Self Cite

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Abstract. The Indian megacity of Delhi suffers from some of the poorest air quality in the world. While ambient NO2 and particulate matter (PM) concentrations have received considerable attention in the city, high ground level ozone (O3) concentrations are an often overlooked component of pollution. O3 can lead to significant ecosystem damage, agricultural crop losses, and adversely affect human health. During October 2018, concentrations of speciated non-methane hydrocarbons volatile organic compounds (C2 – C13), oxygenated volatile organic compounds (o-VOCs), NO, NO2, HONO, CO, SO2, O3, and photolysis rates, were continuously measured at an urban site in Old Delhi. These observations were used to constrain a detailed chemical box model utilising the Master Chemical Mechanism v3.3.1. VOCs and NOx (NO + NO2) were varied in the model to test their impact on local O3 production rates, P(O3), which revealed a VOC-limited chemical regime. When only NOx concentrations were reduced, a significant increase in P(O3) was observed, thus VOC co-reduction approaches must also be considered in pollution abatement strategies. Of the VOCs examined in this work, mean morning P(O3) rates were most sensitive to monoaromatic compounds, followed by monoterpenes and alkenes, where halving their concentrations in the model led to a 15.6 %, 13.1 % and 12.9 % reduction in P(O3), respectively. P(O3) was not sensitive to direct changes in aerosol surface area but was very sensitive to changes in photolysis rates, which may be influenced by future changes in PM concentrations. VOC and NOx concentrations were divided into emission source sectors, as described by the EDGAR v5.0 Global Air Pollutant Emissions and EDGAR v4.3.2_VOC_spec inventories, allowing for the impact of individual emission sources on P(O3) to be investigated. Reducing road transport emissions only, a common strategy in air pollution abatement strategies worldwide, was found to increase P(O3), even when the source was removed in its entirety. Effective reduction in P(O3) was achieved by reducing road transport along with emissions from combustion for manufacturing and process emissions. Modelled P(O3) reduced by ~20 ppb h−1 when these combined sources were halved. This study highlights the importance of reducing VOCs in parallel with NOx and PM in future pollution abatement strategies in Delhi.

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“…To separate these properties either default formatting or a variant may be used, so long as the appropriate changes are made inside the file containing model variables. By default, MCM Kinetic PreProcessor (Sander and Sandu, 2006) formatting is used (Jenkin et al, 1997;Saunders et al, 2003), and PyCHAM has been rigorously tested using schemes and SMILES conversion files from the MCM website (Rickard and Young, 2020).…”

Section: Model Variables and Component Propertiesmentioning

confidence: 99%

“…Peräkylä et al, 2020). As chamber use has multiplied, so too have chamber models, with many now published (Naumann, 2003;Pierce et al, 2008;Lowe et al, 2009;Roldin et al, 2014;Sunol et al, 2018;Topping et al, 2018;Charan et al, 2019;Roldin et al, 2019). Chamber scientists without modelling expertise or access may be limited in the design, interpretation and advancement of both chamber experiments and their contribution to models.…”

Section: Introductionmentioning

confidence: 99%

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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AtChem (version 1), an open-source box model for the Master Chemical Mechanism

Cited by 54 publications

References 55 publications

Ambient Nitro-Aromatic Compounds – Biomass Burning versus Secondary Formation in rural China

Ambient Nitro-Aromatic Compounds – Biomass Burning versus Secondary Formation in rural China

In situ Ozone Production is highly sensitive to Volatile Organic Compounds in the Indian Megacity of Delhi

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

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