Our Modelling

In the IMPeCCABLE project, computational modelling is involved on two different scales. First, at the room chemistry level, where the concentrations of specific chemical species are simulated over time, as a result of chemical reactions occurring in the room. At the other extreme, stock-scale modelling is used to investigate the harmful effects of cooking emissions on people, taking into account the variation in cooking emissions, building types and occupancy, using statistical methods.

Indoor Air Chemistry Modelling

Experiments to determine concentrations of different pollutants produced during cooking and cleaning are a great way of looking at the evolution of specific species over time. However, experiments can also be limited as they only allow you see how the concentrations of specific pollutants evolve over time - there is no way of looking at the chemical reactions that are occurring to produce and destroy the species. This is where computational modelling of underlying chemistry can be really valuable.

In this project, the modelling of indoor air chemistry is carried out using INCHEM-Py [1,2], an open-source, 1-dimensional model, written in Python. INCHEM-Py uses the near-explicit gas-phase chemical scheme of the Master Chemical Mechanism [3], combined with indoor-outdoor exchange, gas-particle partitioning, surface deposition, and internal photolysis, to simulate time-evolved species concentrations. One main purpose of this model in the IMPeCCABLE project is the be able to further interrogate measured data obtained during experimental campaigns. For example, data on chemical emissions from cooking and cleaning experiments can be input into INCHEM-Py, and the resulting chemistry can be tracked over many hours. This means that the production of secondary products, such as formaldehyde, particles and peroxyacyl nitrates (PANs) can be tracked, which is useful because these products are often more harmful to human health than the original emissions. Therefore, understanding how these species are produced can help inform what steps could be taken to mitigate increases in their concentrations during processes such as cooking and cleaning.

An example of using INCHEM-Py to look at the secondary products of an emission of limonene. In the first panel on the left, an emission of limonene is seen in the blue line. In the next two graphs, the blue line shows the resultant concentrations of formaldehyde and particles. In all graphs, the blue line is for the case when limonene is added, the black dashed line is the background concentration of each species, and the pink shaded region is the time period when limonene is being emitted.

Stock-Scale Modelling

We will use CONTAM [1], a ventilation and indoor air quality (IAQ) model, to consider the harm caused to people by exposure to cooking contaminants emitted by cooking. There are 26 million houses in the UK, and so modelling one house wouldn’t capture the variation in all of them. Therefore, we will incorporate CONTAM into a Stock Model.

A stock model accounts for the variation in houses found in the UK by considering a range of factors, such as house type (detached, semi-detached, bungalow, terrace, apartment), location, geometry (height, width, depth), layout (kitchen or kitchen diner), ventilation system (windows, fans, vents), and occupants (family, single occupant, elderly couple etc.). It also accounts for variations in contaminant emissions from cooking. This means we have to perform many simulations of houses. Fortunately, by using statistical techniques, we don’t have to model all 26 million. Instead, by using a Monte-Carlo Simulation (outlined in the diagram below), we may only need 10,000 or so, which will give us the same number of possible outcomes. This will show us the likely range of values for each output as well as those values that are most common. We can then use this information to recommend solutions to minimise harm.

Find out more about CONTAM

[1] CONTAM from NIST