People living in the Global North spend most of their time indoors in the built environment, especially their homes. Indoor air pollution has therefore become a major health concern, particularly in urban environments. Both exposure to poor-quality indoor environments, as well as vulnerability to adverse effects on health and well-being, have a unique geography, varying socially, spatially and temporally.

Yet to date, the measurement of indoor air quality is relatively technical in focus, failing to account for the ways in which indoor environments are complex and varied, shaped by the physical environment, housing stock, policies, household dynamics, incomes and cultural norms. Moreover, because accurate measurement of indoor air pollution requires expensive equipment, specific expertise and plenty of time, existing research is mostly spatially and temporally limited. Assessment of indoor air quality on a large scale is simply difficult.

To bridge this gap, our team sat down together and analysed existing literature to identify the sources of indoor air pollution, for which data already exists, that could be used to build a better understanding of population exposure to indoor air pollution.

In general, we found that we can group the drivers of air pollution into three categories: those originating from the outdoor environment, those originating from the structure of the indoor environments and those originating from people themselves, whether that is their activity in the space, or simply just their existence.

For some of the characteristics inside these groups, no secondary data exists. For example, there is no comprehensive or even suggestive data on population cooking habits, which is one of the most important drivers of indoor air pollution. Nevertheless, plenty of other data exists. We can use data from the National Atmospheric Emissions Inventory (NAEI) on outdoor pollution levels, Energy Performance Certificate (EPC) data to access information about the housing stock, and Census data to understand population characteristics.

How did we do it?

Over 70 variables were collected across multiple data sources, however, only 30 variables are included in the analysis after validity and value checks.

• For the environmental sources of indoor air pollution, we included information on average concentrations of NO², SO², CO, CO², Radon, Black Carbon and Particulate Matter of different sizes.
• For the structural sources of indoor air pollution, we included information on open fireplaces, type of main & secondary heating, property age, CO² emissions and type of ventilation.
• For the human sources of indoor air pollution, we included information on smoking, indoor crowding and deprivation.

To recognise meaningful patterns in the data, we first compiled the variables together into three separate indices and then used cluster analysis to find patterns of similarities for neighbourhoods in England and Wales.

What did we find?

We found 7 distinctive types of neighbourhoods showing variation of the three air pollution sources, visualised for England and Wales, and Bristol (see the map below).

“Urban highs” (violet colour) show areas where there are there are high concentrations of environmental indoor air pollution sources, and at the same time high concentrations of human indoor air pollution sources. These are areas where there is intense traffic or dense road systems, and where the population live in smaller spaces in multi-occupancy households and deprivation is more common.

On the other hand, “Urban extremes of housing vulnerability” (yellow areas) are areas where outdoor pollution or crowding is not particularly an issue; instead, the state and form of the housing stock are of primary concern. This includes a higher concentrations of solid fuel burners to supply heating, open fireplaces, older and diminishing materials in indoor spaces, or inappropriate ventilation.

While we see very distinct urban-rural patterns in indoor air pollution exposure, one of the most important findings is how they vary across populations. Tabulating the three indices with population deprivation shows that the population in highly deprived areas is more likely to be exposed indoors to high levels of pollution from the outdoors (see graph above).

On the other hand, the population in these areas seem to be less likely to be exposed to pollutants connected to the building fabric of their homes. Inappropriate housing is associated with less deprived populations, meaning exposure to indoor air pollution is not only unequal across the socio-economic characteristics of the population, but also according to types of sources. Whilst deprivation can provide an initial indication of the vulnerability to poor indoor air quality in urban areas, the distribution of support and resources solely based on deprivation could overlook the complexity of indoor air pollution challenges.

You can read more about the findings in our journal article or interactive map. Please get in touch with us if you have any questions or want to follow up with some feedback. We will be happy to hear from you.