There are currently inadequacies in our representations of physical processes in indoor air chemistry models. We need to understand and parameterise these processes better if we want our model predictions to improve. This is important as models give us additional information in situations where measurements cannot be made and provide additional insight into chemical processing indoors. Having confidence in models also means that we can use them to explore the impacts of emerging pollutants or predict future IAQ problems. 

Ventilation, light, temperature, relative humidity (RH) and outdoor air pollutant concentrations can all affect predictions of indoor air pollutants, which means that these factors need to be accurately represented in indoor air models.

There are several good sources of information around indoor air chemistry models and what we still need to understand (e.g. our editorial, or Section 3 of the Summary Report that resulted from our WG1 activities). Section 2 of the Final report from WG2, also summarises what we can learn from outdoor air models that could potentially be useful for indoor environments. Morrison et al. (2017) discussed what a framework for indoor air chemistry modelling might look like, whilst Shiraiwa et al. (2019) reported on the development of a new modelling consortium for indoor environments called MOCCIE (MOdelling Consortium for the Chemistry of Indoor Environments): MOCCIE aims to connect models over a range of temporal (from seconds to days) and spatial (from molecular to room) scales. Recent studies have shown that large gains in our understanding are made when modellers are fully integrated into measurement campaigns (e.g. HOMEChem). Measured data can be used to evaluate models, whilst the improved models inform future measurements.

Ventilation can have a large impact on indoor air quality (see Baeza-Romero et al., 2022). Broadly speaking, indoor sources of pollution dominate when ventilation rates are lower, whilst outdoor sources of air pollution become more important when ventilation rates are higher. Whilst high ventilation rates can dilute the products of chemical reactions indoors, they can also refresh oxidant concentrations from outdoors through increased ingress  (e.g. ozone), which can instigate further chemical processing. The location of a building can also be critical. As well as affecting the environmental parameters (such as relative humidity and temperature), it will also determine the outdoor air pollutant concentrations. A house located in a busy city street, will have a very different chemical meteorology to one located in a rural location. Understanding ventilation rates, the type of ventilation (natural versus mechanical, and the recirculation rates for the latter) and outdoor air pollutant concentrations, is vital to understand indoor air quality and to accurately initialise models.

Indoor light is composed of contributions from artificial lights indoors and attenuated sunlight that can move into indoor environments through windows and skylights. The amount of light that can penetrate indoors is influenced by many factors, including the type of window, meteorological conditions, distance from the window/light source and the building direction and location. Based on different conditions, 0.15 % to 30 % of outdoor UV light (300-400 nm) and 0.7 % to 80 % of outdoor visible light (400-750 nm) can be transmitted indoors (Blocquet et al., 2018), with notable consequences for indoor chemistry, particularly for radical concentrations (Wang et al., 2022). The photolysis of many trace gases (e.g. O3 and HCHO) will produce radicals (e.g. OH and HO2 respectively) in the indoor environment. Some of the radicals (e.g. OH) can react with trace gases (e.g. VOC) rapidly in order to form more radicals and a range of secondary species, some of which are harmful to health (e.g. PM). Understanding indoor chemistry therefore requires accurate knowledge of indoor photolysis rates and how these vary spatially, if we are to make models more accurate.

We need better information on the spatial variation of ventilation, light, temperature and RH in buildings, as well as air pollutant concentrations outside buildings. This can be achieved by measuring ventilation rates, light levels, temperature and RH continuously over a substantial period of time and at various points around a building. Ideally, these parameters will be made concurrently with indoor and outdoor air pollutant concentrations. This will help us to understand how each of these parameters affects indoor air quality and hence assess their importance in contributing to model uncertainties.

These physical and chemical measurements need to be made concurrently, at high time and spatial resolution. The respective WG4 articles describe methods to make gas- and particle-phase measurements. For RH, temperature and light levels (and PM), it may be possible to deploy a sensor array that would help to understand variations within rooms/buildings. More information on low cost sensors can be found in our WG4 article here. 

These measurements need to be carried out at a range of locations so that the models are relevant for a wide range of conditions (e.g. climate, location, ventilation regime, outdoor air pollutant concentrations). They should also be made in real buildings, with and without occupants. Some suggestions for considerations when measuring in different types of buildings can be found in the paper that derived from the WG5 activities.