Systemic inequalities in indoor air pollution exposure in London, UK

Deprived communities in many cities are exposed to higher levels of outdoor air pollution, and there is increasing evidence of similar disparities for indoor air pollution exposure. There is a need to understand the drivers for this exposure disparity in order to develop effective interventions aimed at improving population health and reducing health inequities. With a focus on London, UK, this paper assembles evidence to examine why indoor exposure to PM2.5, NOx and CO may disproportionately impact low-income groups. In particular, five factors are explored, namely: housing location and ambient outdoor levels of pollution; housing characteristics, including ventilation properties and internal sources of pollution; occupant behaviours; time spent indoors; and underlying health conditions. Evidence is drawn from various sources, including building physics models, modelled outdoor air pollution levels, time–activity surveys, housing stock surveys, geographical data, and peer-reviewed research. A systems framework is then proposed to integrate these factors, highlighting how exposure to high levels of indoor air pollution in low-income homes is in large part due to factors beyond the control of occupants, and is therefore an area of systemic inequality. Policy Relevance There is increasing public and political awareness of the impact of air pollution on public health. Strong scientific evidence links exposure to air pollution with morbidity and mortality. Deprived communities may be more affected, however, with limited evidence on how deprivation may influence their personal exposure to air pollution, both outdoors and indoors. This paper describes different factors that may lead to low-income households being exposed to higher levels of indoor air pollution than the general population, using available data and models for London (i.e. living in areas of higher outdoor air pollution, in poor-quality housing, undertaking more pollution-generating activities indoors and spending more time indoors). A systems approach is used to show how these factors lead to systemic exposure inequalities, with low-income households having limited opportunities to improve their indoor air quality. This paper can inform actions and public policies to reduce environmental health inequalities, considering both indoor and outdoor air.


APPENDIX 1
Simulations were run using EnergyPlus for using a weather file with typical conditions for London City. Building geometries are based on archetypes used in Taylor et al (2016), with permeabilities taken to be the average of each London dwelling type based on previous stock modelling Taylor et al (2019).
Indoor emission rates, deposition rates and production schedules are outlined below in Table S1. Outdoor PM2.5 levels were kept at a static 13.3 μg/m 3 across all models, representing the London 2019 average ambient concentration (GLA, 2020).
Smoking was assumed to occur eleven times per day, in line with ONS data which suggests current UK smokers consume an average of 11.3 cigarettes per day (ONS, 2019). Cooking schedules are based on those from Taylor et al (2016).
Extract fans, if present, were modelled to be used while cooking, with an extract rate according to building regulations (HM Government, 2013). No extra ventilation was modelled during smoking. Window opening was modelled when indoor temperatures exceeded 24C during the summer.

APPENDIX 2
This appendix shows the formation of the systems diagram. Figure S1. The relationship between traffic and industry and outdoor air pollution, described in Section 2.1.
Variables with black outlines are those which have empirical evidence to support socioeconomic differences in levels in London. Here, increases in the amount of traffic and industry will lead to increases in outdoor air pollution and a gradually reduction in the quality and perceived quality of the surrounding environment. There will be a more immediate increase in outdoor air pollution. Summarised evidence supports socioeconomic disparities in traffic, outdoor air pollution, and perceived quality of the outdoor environment. Figure S2. The relationship between housing quality and indoor air pollution, described in Section 2.2.
Variables with black outlines are those which have empirical evidence to support socioeconomic differences in levels in London, while those with grey outlines have only limited data to support the relationship. Summarised evidence indicates lower frequencies of working extract fans, reduced infiltration rates, and greater housing density which can limit the number of openable windows and can lead to pollution moving between adjoining dwellings. There is some limited evidence to suggest that some pollution-emitting appliances may be less well maintained. Figure S3. The relationship between occupant behaviours and indoor air pollution, described in Section 2.3.
Variables with black outlines are those which have empirical evidence to support socioeconomic differences in levels in London, while those with grey outlines have only limited data to support the relationship. There is evidence that low SES households are more likely to have a smoker resident, and that low SES households may spend longer cooking indoors to accommodate larger households. There is limited evidence that suggests that low SES households may open their windows less due to security concerns. Figure S4. The relationship between occupant time at home and indoor air pollution exposure, described in Section 2.4.
Variables with black outlines are those which have empirical evidence to support socioeconomic differences in levels in London, while those with grey outlines have only limited data to support the relationship. The analysis of the time activity data indicates that low SES individuals spend a greater amount of time at home. This may be driven by the lower perception or quality of the surrounding environment. Figure S5. The relationship between occupant health air pollution exposure, described in Section 2.5.
Variables with black outlines are those which have empirical evidence to support socioeconomic differences in levels in London, while those with grey outlines have only limited data to support the relationship. S5(a): Balancing loop B3 shows how increased exposures to outdoor air pollution can gradually lead to increased health issues, resulting in individuals spending more time at home and a consequent reduction in outdoor air pollution exposures. Evidence has been summarised showing SES disparities in time at home and underlying health issues.

S5(b):
Reinforcing loop R1 that shows how an increased time at home may decrease the time spent outdoors, gradually increasing health risks due to -for example -a lack of physical activity, which further increases time at home.

S5(c):
Reinforcing loop R2 illustrates how increased time at home due to air-pollution related health problems reinforces exposures to indoor air pollution. Therefore, as health issues due to indoor and outdoor air pollution increase, the exposure balance shifts towards the indoors from the outdoors.

S5(d):
Reinforcing loop R3 describes how a low perception of their local environment may gradually lead to individuals spending more time indoors, further reducing their perception of their local environment.