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Published 22.11.2018

Ambient air pollution and household air pollution are two sides of the same coin, and it may be misleading to treat them as separate health risk factors, according to new studies.

• Change in household fuels dominates the decrease in PM_2.5 exposure and premature mortality in China in 2005–2015
• Aunan K, Ma Q, Lund MT, Wang S. Population-weighted exposure to PM 2.5 pollution in China: An integrated approach. Environment International 2018b; 120: 111-120

At the first World Health Organization (WHO) Global Conference on Air Pollution and Health, held in Geneva recently, participants recommended an aspirational goal of reducing the number of deaths from air pollution by two thirds by 2030. Estimates by the WHO and the Global Burden of Disease study show that air pollution causes 7 million deaths per year globally, i.e. on par with the toll of smoking.¹ Notwithstanding what it will take to reach this goal, how can we estimate the progress?

Recently published papers by an international team of researchers, including from CICERO, may shed some light on the question. The papers challenge the approach taken in current studies of the health burden from air pollution, including the Global Burden of Disease Study, and proposes an integrated approach to exposure and health impact assessment. The first study (Aunan et al., 2018b) argues that ambient air pollution and household air pollution are two sides of the same coin and that it may be misleading to treat them as separate health risk factors as is currently the case.

So far, studies of the global health burden of air pollution (for which fine particles, PM_2.5, is the major indicator), have treated exposure to ambient air pollution and household air pollution as separate risk factors. Ambient air pollution refers to PM_2.5 pollution in outdoor environments. Household air pollution on the other hand refers to indoor PM_2.5 pollution in homes and neighbourhoods caused by smoke from household fuels such as wood and coal, due to incomplete fuel combustion in simple stoves.

Thus, of the 7 million annual deaths caused by air pollution globally, around 60% of the burden is estimated to be due to exposure in ambient air, whereas the rest is attributed to household air pollution. Estimates of the ambient air pollution burden are usually based on global atmospheric chemistry models, often in combination with satellite data and ground level monitoring data, and geographically resolved population and health data. Estimates of the household air pollution burden, on the other hand, are based on measurements of the exposure to PM_2.5 found in populations using solid household fuels and statistical data on the use of such fuels. Importantly, the assumption is that ambient air pollution contributes little to the exposure in these populations.

Reasons to leave the dichotomous approach

In many regions, including China and India, household air pollution may come in addition to high levels of ambient air pollution, thus PM_2.5 exposure for solid fuel users can differ substantially depending on the ambient air pollution level where they live. Moreover, emissions from household stoves may in themselves be an important source of ambient air pollution. For instance, recent estimates show that emissions from residential stoves may be responsible for more than 50% of the population-weighted exposure to ambient PM_2.5 in India (Conibear et al., 2018). The corresponding figure for China is in the range 20-40%, with estimates above 50% for certain regions (Aunan et al., 2018b). Finally, studies have found that the exposure-response relationship between PM_2.5 exposure and mortality for important diseases associated with PM_2.5 pollution is relatively steep at low exposures and levels off at higher exposure (Burnett et al., 2014). The curvilinear feature of these so-called ‘integrated exposure-response functions’ implies that there is a high risk of double-counting when the disease burden for ambient and household air pollution are calculated separately. It also implies that air pollution abatement policies, whether they target ambient or household air pollution, may be credited a higher benefit than what can realistically be obtained on the ground. These problems are also recognized by the Global Burden of Disease study.

An integrated approach to exposure and health burden estimation

Taking as the starting point that the total exposure to PM_2.5 is what matters for health, the recent studies (Aunan et al., 2018b; Zhao et al., 2018a) developed a method for estimating the total annual mean population-weighted exposure to PM_2.5. The exposure metric is denoted ‘integrated population-weighted exposure’ (IPWE) and was applied to evaluate the progress when it comes to air pollution related mortality in China in the period 2005-2015 and what are main sources contributing to the mortality burden. The results also revealed large inequities in air pollution exposure across rural and urban populations and across regions. In fact, the estimated IPWE in rural populations was on average 2-3 times the level in urban populations. Worst off are probably people living close to industry and polluted cities and still depending on solid household fuels (Aunan et al., 2018a). This is the case, for instance, for many people in the heavily polluted Hebei province (fig. 1) (Aunan et al., 2018b).

Figure 1. Contribution to population-weighted PM_2.5 exposure in urban and rural settings in selected Chinese provinces. Source: Aunan et al 2018b.

Not a straight line from emission reductions to health benefits

Since 2005, the Chinese government has implemented substantial emission control policies to reduce emissions of air pollutants. The focus of the control policies was initially placed on power plants and transportation and extended to industrial sources after 2010. In 2013, China issued the “Air Pollution Prevention and Control Action Plan” for 2013-2017, which was followed up by the “2018-2020 Three-year Action Plan for Winning the Blue Sky War”. The policies have led to impressive reductions in emissions of SO2 and particulate matter components. The annual mean ambient concentration of PM_2.5 was reduced by 35% on average for China’s 74 key cities in the period 2013-2017. More than half of this concentration reduction was due to reduced emissions from industries and coal fired boilers. Vehicle emission control had little impact, contributing only 2% to the reduction according to estimates from Tsinghua University in Beijing.

While the focus on industries and energy production has been effective in improving ambient air quality in many Chinese cities, the study by Zhao et al (2018) shows that it may have been less effective in reducing the PM_2.5 mortality burden. Using the total exposure metric, the study found that the IPWE in the urban population on average was reduced from about 90 microgr/m³ in 2005 to about 60 microgr/m³ in 2015. The corresponding figures for the rural population were 250 microgr/m³ in 2005 and 150 microgr/m³ in 2015. In total, the reduced exposure avoided about 500.000 annual premature deaths. The lion’s share of the reduced exposure and mortality burden was, however, not related to reduced emissions from industries and energy production, but rather to the transition from dirty to clean household fuels, to a large extent caused by internal migration and urbanization (rural residents generally get access to cleaner fuels as they migrate to cities or rural settlements are urbanized). For the Chinese population on average, this energy transition alone led to an IPWE reduction of more than 40%. Emission reductions in industry, power plants and other non-residential sources on the other hand lead to a meager 6% reduction in IPWE and had a limited impact on the overall mortality burden. Not surprisingly, emission reductions in non-residential sectors had larger health benefits for the urban population as compared to the rural.

More realistic estimates?

Approaches that recognize the links between ambient and household air pollution could improve the basis for policy analyses and interventions aiming at reducing the health burden from PM_2.5 exposure. It could also contribute to promoting equity considerations in air pollution policies. Finally, it could provide more realistic estimates of the progress in reducing the global health burden from air pollution.

There are inherently large uncertainties in estimates of air pollution health burden as numerous factors influence exposure and vulnerability. Building a robust knowledge basis for total exposure assessment will require substantive efforts. For instance, more reliable data of ambient air pollution in the world’s many towns and villages is needed, as well as intensified research to characterize the source contribution to exposure among rural and urban populations alike.

One may argue that, in spite of some inconsistencies, treating household air pollution as a risk factor separate from ambient air pollution is useful as it sheds light on an underappreciated source of global ill health. This may be true, but so far it has had little impact on air pollution policies across the world, which still mostly focus on urban ambient air quality and emission sources such as traffic, industry and power production. An exception may be India, where large programs are being rolled out to promote switch from biomass fuels to gas (liquified petroleum gas, LPG) with the expressed aim of cleaning up the indoor environment in poor people’s homes. In China, the second phase of the “war on air pollution”, covering the period 2018-2020, still has no mentioning of household air pollution. However, as the Chinese government to an increasing extent is concerned about the impact that household coal stoves has on ambient air in the Beijing region, programs are now instigated to provide gas in surrounding rural areas. In India as well as in China, the policies involving household energy use will undoubtedly affect both ambient and household air pollution. Or, simply, the population exposure to PM_2.5.

[1] A recent revision by the Institute for Health Metrics and Evaluation (IHME) suggest the figure may be around 5 million. (https://vizhub.healthdata.org/gbd-compare/ )