PyBox: An automated box-model generator for atmospheric chemistry and aerosol simulations.

Abstract

SummaryAir pollution and climate change are two of the biggest multidisciplinary challenges in society today. The need to understand the chemical and physical processes in the atmosphere that dictate the impacts of both has created a wide range of research platforms. These include numerical models. Volatile organic compounds (VOCs), emitted from both natural and anthropogenic sources, are oxidised in the atmosphere to form lower-volatility species that form organic particulate matter in the atmosphere through gas-to-particle partitioning. Chemical mechanisms, such as the Master Chemical Mechanism (MCM) (Jenkin, Saunders, Wagner, & Pilling, 2003), have been built to hold information of all relevant species and reactions in the atmosphere.However, it is becoming extremely hard to develop models that can respond to our growing body of knowledge since these mechanisms now treat thousands of species and tens of thousands of reactions. Researchers need to simulate the evolution of each individual species over a range of time-scales and ambient conditions. Manually setting up the relevant ordinary differential equations definitions [ODEs] and associated solvers would present a huge challenge given the number of compounds and equations involved. In addition, it is important to test sensitivity to rate coefficients or evaluate the impact of emerging laboratory data that might identify new reaction pathways or new, improved rate coefficients. Added to this complexity is the need to account for gas-to-particle partitioning of each compound. The atmosphere has varying concentrations of particulate matter that might act as a condensational sink for each compound. Predicting that partitioning requires calculations of molecular properties including saturation vapour pressures. Automation is essential, not least to ensure the gas phase chemistry is accounted for alongside the gas-to-particle partitioning and reproducibility is ensured. This is the driver behind PyBox.

GenChem v1.0 – a chemical pre-processing and testing system for atmospheric modelling

“…POM are assumed inert, consistent with the PM_VBS_EmChem19 scheme discussed below. EC emissions are further divided into "new" and "age" components, to reflect the level of hydrophobicity (Tsyro et al, 2007;Genberg et al, 2013). In some inventories primary sulfate is also provided, represented here as pSO4 (if pSO4 is used, remPPM will then represent all PM components except EC, POM and pSO4).…”

PyCHAM: CHemistry with Aerosol Microphysics in Python

“…Although several box models have already been published (Pierce et al, 2008;Roldin et al, 2019Roldin et al, , 2014Sunol, Charan, & Seinfeld, 2018), PyCHAM is novel in its ease of accessibility and utility. To the best of our knowledge, PyBOX (Topping, Connolly, & Reid, 2018), which formed the basis of PyCHAM, and AtChem (Borońska et al, 2019), are the only other open source box models with graphical user interfaces. However, PyCHAM provides a considerable upgrade to the functionality of both prior models, as AtChem only simulates gas-phase photochemistry and PyBOX has limited particle effects, whilst PyCHAM models significantly more particle microphysics (detailed below) and wall effects, in addition to gas-phase photochemistry.…”

“…PyCHAM takes a sectional approach to particulates, dividing particles into a number of size bins and treating their changing size using the moving-centre approach (Jacobson, 2005). It builds upon PyBOX (Topping et al, 2018), which did not include coagulation, nucleation or a sectional method.…”

Photochemical impacts of haze pollution in an urban environment

“…To evaluate the potential photochemical impact on oxidants, a simple experiment was performed using a photochemical box model incorporating the generic reaction set for volatile organic compound oxidation (Topping et al, 2018). The response of O 3 concentrations to the presence of aerosols is similar to that of J[O 1 D] in both seasons with the biggest reductions (12.0 % or larger) seen in the lowest 500 m, see Figure 6.…”