Global premature mortality due to anthropogenic outdoor air pollution and the contribution of past climate change

Since the industrial revolution, human activities have significantly increased the concentrations of ozone and fine particulate matter (with aerodynamic diameter less than 2.5 μm, PM_2.5) in both urban and rural regions (Schulz et al 2006, Parrish et al 2012). These changes have been driven by direct changes in air pollutant emissions, and, because climate change also influences air quality, a component of the changes in anthropogenic air pollution may result from past climate change. Climate change influences air quality through several mechanisms, including changes in photochemical reaction rates, biogenic emissions, deposition, and atmospheric circulation (Jacob and Winner 2009, Weaver et al 2009, Fiore et al 2012).

Epidemiological studies have shown that ozone and PM_2.5 have significant influences on human health, including premature mortality. Evidence for mortality influences comes from a large number of daily time series studies (e.g., Bell et al 2004, HEI 2004). There is also evidence for chronic effects on mortality through several large cohort studies for PM_2.5 (Hoek et al 2002, Krewski et al 2009, Lepeule et al 2012), while evidence for chronic effects of ozone derives mainly from one study (Jerrett et al 2009).

Past research to estimate the global burden of disease due to outdoor air pollution has used a variety of methods. Cohen et al (2004) estimated 800 000 premature deaths annually attributed to urban PM_2.5 globally, based on surface measurements. Accounting for both urban and rural regions globally, Anenberg et al (2010) used output from a global atmospheric model to estimate 3.7 ± 1.0 million deaths annually due to anthropogenic (present-day relative to preindustrial) changes in PM_2.5 and 0.7 ± 0.3 million due to ozone. Brauer et al (2012) used high-resolution satellite observations of PM_2.5 together with a global atmospheric model and an extensive compilation of surface measurements to better represent global air pollution exposure. These exposure estimates were then used to estimate 3.2 ± 0.4 million premature deaths due to PM_2.5 and 150 000 (50 000 to 270 000) due to ozone (Lim et al 2012).

Few studies have assessed the effects of climate change on human health via changes in air quality. Of those, the focus has been on the influence of future climate change, including assessments on a metropolitan scale (Knowlton et al 2004, Sheffield et al 2011), in the US (Bell et al 2007, Tagaris et al 2009, Post et al 2012), and globally (West et al 2007, Selin et al 2009). Of these studies, only Post et al (2012) use a multi-model ensemble, showing significant variability in estimates of ozone-related mortality attributed to climate change depending on the atmospheric model results used. For the effects of past climate change on air quality and human health, Orru et al (2013) evaluated regional effects of ozone in Europe for both the recent past and the future. Fang et al (2013) conducted a global analysis of past climate change based on simulations from a single atmospheric model (GFDL-AM3); those model simulations are included in the multi-model ensemble used here.

Here we assess the burden of global anthropogenic air pollution on premature human mortality, and the contribution of past changes in climate to the total burden, using simulations from an ensemble of global coupled chemistry–climate models (Lamarque et al 2013). Our approach to estimate the global burden of air pollution on mortality expands on that of Anenberg et al (2010) by using an ensemble of model estimates of both present-day and preindustrial air pollution. We then use simulations that combine present-day emissions and preindustrial climate to separate the influences of past climate change on air quality and human health.

2.1. Modeled ozone and PM_2.5 surface concentrations

2.2. Health impact assessment

Figures 1 and 2 show estimates of premature mortality due to anthropogenic ozone and PM_2.5 for each model, and changes in concentration underlying these estimates are presented in the supporting information (available at stacks.iop.org/ERL/8/034005/mmedia). The average estimate across the 14 models suggests that 470 000 premature respiratory deaths occur globally and annually due to anthropogenic increases in ozone, with no low-concentration threshold. Accounting for both the 95% confidence interval (CI) on the CRF, reported by Jerrett et al (2009), and the distribution of results from the 14 models, using Monte Carlo sampling, yields a 95% CI of 140 000 to 900 000 (uncertainty ranges reported hereafter follow the same methods). Global ozone mortality is about 20% lower when a low-concentration threshold is used. In figure 3 and table 1, ozone-related mortality is widespread globally, as ozone has increased essentially everywhere from human activities, but is greatest in highly populated and highly polluted areas of India and East Asia, which account for 68% of the global total.

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For PM_2.5 estimated as a sum of species, the 6-model average indicates that 2.1 (1.3 to 3.0) million premature CPD and LC deaths occur globally and annually due to anthropogenic increases, with no low-concentration threshold. Of these deaths, 93% are related to CPD and 7% to LC. Relative to ozone, there is less scatter among the models, with a coefficient of variation (σ/μ) among models of 0.10 for PM_2.5, compared to 0.26 for ozone. For both PM_2.5 and ozone, the uncertainty in the CRF is greater than the uncertainty over the range of models. Global PM_2.5 mortality is 11% lower for the multi-model average when using a low-concentration threshold of 5.8 μg m⁻³, and is 19% lower when using PM_2.5 as reported by 4 models. While the formulas for estimating PM_2.5 differ between models, the larger change in concentrations when adding species is mainly due to the omission of nitrate in the PM_2.5 reported by the models. Large differences may also result from differences in how dust and sea salt are added to PM_2.5, as models that calculate PM_2.5 use size-resolved information and so are likely more accurate than the common formula used here. PM_2.5-related mortality is widespread in populated regions, principally in East Asia and India, but also in Southeast Asia, Europe, and the Former Soviet Union. However, some locations are modeled as having a decrease in PM_2.5 relative to 1850, including the southeast US and parts of Latin America, and small regions elsewhere. In the southeast US, this decrease is caused by reductions in biomass burning relative to 1850, as changes in primary organic carbon are primarily responsible for the decrease, which also is apparent in the radiative forcing due to biomass burning aerosols (Shindell et al 2013). Local decreases in India and Africa likely relate to past climate change (see supporting information available at stacks.iop.org/ERL/8/034005/mmedia).

These estimates of the global burden are smaller than those reported by Anenberg et al (2010). Since Anenberg et al (2010) used the same CRFs and only small differences in global population and baseline mortality rates, the lesser estimated mortality is mainly due to differences in modeled concentrations. While the model used in that study differs from the ensemble used here, the greater difference is likely to be the different emissions used for both the present-day and preindustrial simulations (Lamarque et al 2010, Fang et al 2013).

These global burden estimates are also greater for ozone but less for PM_2.5 than were estimated in the most recent Global Burden of Disease study (Lim et al 2012). For ozone, these differences are likely explained by the fact that modeled 1850 ozone (table S5 available at stacks.iop.org/ERL/8/034005/mmedia) is lower than the assumed counterfactual low concentration of Lim et al (2012) of 37.6 ppb. For PM_2.5, the modeled 1850 concentrations are close to the counterfactual concentrations used by Lim et al (2012) of 7.3 μg m⁻³; the smaller estimate here may be due to differences in CRFs.

Our estimates using results from the GFDL-AM3 simulations of Fang et al (2013) are 45% higher for ozone mortality and 24% higher for PM_2.5 mortality than those reported by Fang et al (2013); this difference is accounted for mainly by the smaller global population aged 30 years and older for the year 2000 used in that study and, to a lesser extent, differences in baseline mortality rates.

The 9-model average estimate of the effect of past climate change on ozone respiratory mortality is 1500 (−20 000 to 27 000) deaths annually with no threshold (figure 4). There is large variability among models, with six of nine models suggesting that past climate change caused ozone mortality to increase. In figure 5 and table 2, deaths are greatest in East Asia for the multi-model average, but also positive in North America and parts of India. In figure 6, most models predict ozone decreases due to climate change in tropical regions and over oceans. This likely results from increases in water vapor, which causes greater production of HO_x radicals and greater destruction of ozone. Over polluted regions, however, ozone increases from faster reaction rates and meteorological changes (Jacob and Winner 2009). Because most models reported several years of simulations, the variability between models is not likely a result of inter-annual meteorological variability.

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For PM_2.5, the 5-model average mortality (CPD + LC) attributed to past climate change is 2200 (−350 000 to 140 000) deaths annually, with no threshold and estimating PM_2.5 as a sum of species (figure 7). Four of the five models estimate an increase in deaths, but the average is decreased by one model (HadGEM2) that estimates −283 000 deaths from PM_2.5 decreases due to climate change. The multi-model median mortality is 61 000 deaths annually, and, if HadGEM2 is excluded, the multi-model average is 74 000 (30 000 to 140 000) deaths yr⁻¹. Average mortality is higher when using PM_2.5 from the four models that reported it, and without the large negative uncertainty, as HadGEM2 did not report PM_2.5. In figure 5 and table 2, the 5-model average shows that past climate change caused the largest increases in PM_2.5 premature mortality in East Asia, and notable increases elsewhere including North America, but decreases in India and parts of Africa, the Former Soviet Union and Europe. The strong negative mortality estimate from HadGEM2 is the result of PM_2.5 decreases over India, driven by changes in dust, as India has a large exposed population. Figure 6 shows that most models predict an increase in PM_2.5 over India due to climate change, with only HadGEM2 being a strong exception. Most models predict increases in PM_2.5 over land, but it is difficult to explain the different regional patterns of concentration changes due to past climate change across the different models. These inter-model differences for both ozone and PM_2.5 are likely driven by the processes included in the different models, such as whether and how emissions from dust, vegetation, and lightning are modified as a result of climate change, and differences in how the models represent past climate change and its influences on photochemistry and pollutant transport and removal.

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We estimate that in the present-day, anthropogenic changes to air pollutant concentrations since the preindustrial are associated annually with 470 000 (95% CI, 140 000 to 900 000) premature respiratory deaths related to ozone, and 2.1 (1.3 to 3.0) million CPD and LC deaths related to PM_2.5. Our estimates differ from those of Lim et al (2012) in that we estimate mortality for changes in air pollution relative to the modeled preindustrial conditions, rather than using a counterfactual low concentration. Relative to Anenberg et al (2010), our results also differ mainly because of the different emissions used in the models for preindustrial and present-day conditions, and by using modeled concentrations from an ensemble of models rather than a single model.

There is significant variability in mortality estimates driven by different atmospheric models, even though these models used very similar anthropogenic emissions, highlighting the uncertainty in basing results on a single model. Variability among models is higher for ozone than for PM_2.5, but for both pollutants, it contributes less to overall uncertainty than the uncertainty in CRFs. The uncertainty in CRFs is understated because it does not account for the full range over the literature—e.g., use of the CRF for PM_2.5 from Lepeule et al (2012) would lead to higher mortality estimates. The relative magnitude of results using different CRFs and with low-concentration thresholds, analyzed by Anenberg et al (2010), would also apply here. As for previous studies that estimate the mortality burden of outdoor air pollution, our methods likely underestimate the true burden because we have limited the evaluation to adults aged 30 and older, and base the analysis on coarse-resolution models that likely underestimate exposure, particularly for PM_2.5 in urban areas (Punger and West 2013). On the other hand, recent studies suggest that the relationship between PM_2.5 and mortality may flatten at high concentrations (Pope et al 2011), suggesting that we may overestimate PM_2.5 mortality in regions with very high concentrations. We also caution that there are large uncertainties in applying CRFs from the US globally.

Air pollution-related mortality due to past climate change is shown to be significantly smaller than the total anthropogenic burden—i.e., anthropogenic increases in emissions likely have had a much greater influence on air pollutant concentrations than past climate change. We estimate here that 1500 (−20 000 to 27 000) premature respiratory deaths related to ozone and 2200 (−350 000 to 140 000) CPD and LC deaths related to PM_2.5 occur each year due to past climate change. The large uncertainties reflect significant variability among different atmospheric models, with some models estimating an overall decrease in mortality from past climate change. The multi-model averages for both ozone and PM_2.5 mortality are very small by coincidence, as the results for individual models show a large range of positive and negative values.

Consequently, it cannot be clearly concluded that past climate change has increased air pollution mortality. This large variability among models suggests that using a single model to represent past climate change may have significant uncertainties. This conclusion agrees with that of Post et al (2012) who analyzed the effects of future climate change on air pollution mortality in the US from an ensemble of atmospheric models. As models continue to develop and more comprehensively represent the mechanisms by which climate change might influence air quality, we should expect that large differences between estimates based on different models will likely persist.

This work was supported by fellowship SFRH/BD/62759/2009 (to RAS) from the Portuguese Foundation for Science and Technology (FCT), and by the International Council for Clean Transportation. Acknowledgments of funding for the modeling groups participating in ACCMIP can be found in previous publications (e.g., Lamarque et al 2013).