Analysis of inequalities in personal exposure to PM2.5: A modelling study for the Greater London school-aged population.

Exposure to air pollution can lead to negative health impacts, with children highly susceptible due to their immature immune and lung systems. Childhood exposure may vary by socio-economic status (SES) due to differences in both outdoor and indoor air pollution levels, the latter of which depends on, for example, building quality, overcrowding and occupant behaviours; however, exposure estimates typically rely on the outdoor component only. Quantifying population exposure across SES requires accounting for variations in time-activity patterns, outdoor air pollution concentrations, and concentrations in indoor microenvironments that account for pollution-generating occupant behaviours and building characteristics. Here, we present a model that estimates personal exposure to PM2.5 for ~1.3 million children aged 4-16 years old in the Greater London region from different income groups. The model combines 1) A national time-activity database, which gives the percentage of each group in different residential and non-residential microenvironments throughout a typical day; 2) Distributions of modelled outdoor PM2.5 concentrations; 3) Detailed estimates of domestic indoor concentrations for different housing and occupant typologies from the building physics model, EnergyPlus, and; 4) Non-domestic concentrations derived from a mass-balance approach. The results show differences in personal exposure across socio-economic groups for children, where the median daily exposure across all scenarios (winter/summer and weekends/weekdays) is 17.2 µg/m3 (95%CIs: 12.1 µg/m3-41.2 µg/m3) for children from households in the lowest income quintile versus 14.5 µg/m3 (95%CIs: 11.5 µg/m3 - 27.9 µg/m3) for those in the highest income quintile. Though those from lower-income homes generally fare worse, approximately 57 % of London's school-aged population across all income groups, equivalent to 761,976 children, have a median daily exposure which exceeds guideline 24-h limits set by the World Health Organisation. The findings suggest residential indoor sources of PM2.5 are a large contributor to personal exposure for school children in London. Interventions to reduce indoor exposure in the home (for example, via the maintenance of kitchen extract ventilation and transition to cleaner cooking fuels) should therefore be prioritised along with the continued mitigation of outdoor sources in Greater London.

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.

Air quality around schools: Part II - Mapping PM2.5 concentrations and inequality analysis.

Exposure to air pollution poses a significant risk to children's health. However, there is not currently a full and clear understanding of how many schools in England are in locations with high concentrations of air pollutants, and few studies have examined potential associations between air quality outside schools and socio-economic inequalities. To address these gaps, in this part of our study we used modelled air pollution concentrations, as well as monitoring data, to estimate how many schools in England are co-located with levels of annual mean PM2.5 that exceed the WHO recommended annual mean limit of 10 µgm-3, and matched school annual mean PM2.5 concentrations to inequality metrics. We assessed the limitations of our methodology by carrying out a sensitivity analysis using a small patch of high-resolution air pollution data generated using a data extrapolation method. Mapping of modelled annual mean concentrations at school locations indicates that around 7800 schools in England - over a third of schools - are in areas where annual mean PM2.5 in 2017 exceeded the WHO recommended guideline (10 µgm-3). Currently over 3.3 million pupils are attending these schools. We also found that air pollution outside schools is likely to be compounding existing childhood socio-economic disadvantage. Schools in areas with high annual mean PM2.5 levels (>12 µgm-3) had a significantly higher median intake of pupils on free school meals (17.8%) compared to schools in low PM2.5 areas (<6 µgm-3 PM2.5, 6.5% on free school meals). Schools in the highest PM2.5 concentration range had significantly higher ethnic minority pupil proportion (78.3%) compared to schools in the lowest concentration range (6.8%). We also found that in major urban conurbations, ethnically diverse schools with high PM2.5 concentrations are more likely to be near major roads, and less likely to be near significant greenspace, compared to less ethnically diverse schools in areas with lower PM2.5 levels.

Exposure to indoor air pollution across socio-economic groups in high-income countries: A scoping review of the literature and a modelling methodology.

Disparities in outdoor air pollution exposure between individuals of differing socio-economic status is a growing area of research, widely explored in the environmental health literature. However, in developed countries, around 80% of time is spent indoors, meaning indoor air pollution may be a better proxy for personal exposure. Building characteristics - such as build quality, volume and ventilation - and occupant behaviour, mean indoor air pollution may also vary across socio-economic groups, leading to health inequalities. Much of the existing literature has focused on inequalities in exposure to outdoor air pollution, and there is thus a lack of an evidence base reviewing data for indoor environments. In this study, a scoping review of the literature on indoor air pollution exposures across different socio-economic groups is performed, examining evidence from both monitoring and modelling studies in the developed world. The literature was reviewed, identifying different indoor pollutants, definitions for socio-economic status and pre- and post- housing interventions. Based on the review, the study proposes a modelling methodology for evaluating the effects of environmental policies on different socio-economic populations. Using a sample size calculation, obstacles in obtaining sufficiently large samples of monitored data are demonstrated. A modelling framework for the rapid quantification of daily home exposure is then outlined as a proof of concept. While significant additional research is required to examine inequalities in indoor exposures, modelling approaches may provide opportunities to quantify exposure disparities due to housing and behaviours across populations of different socio-economic status.

Advancing tools to promote health equity across European Union regions: the EURO-HEALTHY project.

BACKGROUND: Population health measurements are recognised as appropriate tools to support public health monitoring. Yet, there is still a lack of tools that offer a basis for policy appraisal and for foreseeing impacts on health equity. In the context of persistent regional inequalities, it is critical to ascertain which regions are performing best, which factors might shape future health outcomes and where there is room for improvement. METHODS: Under the EURO-HEALTHY project, tools combining the technical elements of multi-criteria value models and the social elements of participatory processes were developed to measure health in multiple dimensions and to inform policies. The flagship tool is the Population Health Index (PHI), a multidimensional measure that evaluates health from the lens of equity in health determinants and health outcomes, further divided into sub-indices. Foresight tools for policy analysis were also developed, namely: (1) scenarios of future patterns of population health in Europe in 2030, combining group elicitation with the Extreme-World method and (2) a multi-criteria evaluation framework informing policy appraisal (case study of Lisbon). Finally, a WebGIS was built to map and communicate the results to wider audiences. RESULTS: The Population Health Index was applied to all European Union (EU) regions, indicating which regions are lagging behind and where investments are most needed to close the health gap. Three scenarios for 2030 were produced - (1) the 'Failing Europe' scenario (worst case/increasing inequalities), (2) the 'Sustainable Prosperity' scenario (best case/decreasing inequalities) and (3) the 'Being Stuck' scenario (the EU and Member States maintain the status quo). Finally, the policy appraisal exercise conducted in Lisbon illustrates which policies have higher potential to improve health and how their feasibility can change according to different scenarios. CONCLUSIONS: The article makes a theoretical and practical contribution to the field of population health. Theoretically, it contributes to the conceptualisation of health in a broader sense by advancing a model able to integrate multiple aspects of health, including health outcomes and multisectoral determinants. Empirically, the model and tools are closely tied to what is measurable when using the EU context but offering opportunities to be upscaled to other settings.

Population Health Inequalities Across and Within European Metropolitan Areas through the Lens of the EURO-HEALTHY Population Health Index.

The different geographical contexts seen in European metropolitan areas are reflected in the uneven distribution of health risk factors for the population. Accumulating evidence on multiple health determinants point to the importance of individual, social, economic, physical and built environment features, which can be shaped by the local authorities. The complexity of measuring health, which at the same time underscores the level of intra-urban inequalities, calls for integrated and multidimensional approaches. The aim of this study is to analyse inequalities in health determinants and health outcomes across and within nine metropolitan areas: Athens, Barcelona, Berlin-Brandenburg, Brussels, Lisbon, London, Prague, Stockholm and Turin. We use the EURO-HEALTHY Population Health Index (PHI), a tool that measures health in two components: Health Determinants and Health Outcomes. The application of this tool revealed important inequalities between metropolitan areas: Better scores were found in Northern cities when compared with their Southern and Eastern counterparts in both components. The analysis of geographical patterns within metropolitan areas showed that there are intra-urban inequalities, and, in most cities, they appear to form spatial clusters. Identifying which urban areas are measurably worse off, in either Health Determinants or Health Outcomes, or both, provides a basis for redirecting local action and for ongoing comparisons with other metropolitan areas.