Disease burden of air pollution

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Question: What is the disease burden of fine particles globally? Answer: This variable is a summary of two previous assessments, namely Lelieveld et al 2015 and Global Burden of disease 2010. Also, this page is a place for discussions about the methods and results of the assessment. Assessment of the global burden of disease is based on epidemiological cohort studies that connect premature mortality to a wide range of causes, including the long-term health impacts of ozone and fine particulate matter with a diameter smaller than 2.5 micrometres (PM2.5). It has proved difficult to quantify premature mortality related to air pollution, notably in regions where air quality is not monitored, and also because the toxicity of particles from various sources may vary. Lelieveld et al use a global atmospheric chemistry model to investigate the link between premature mortality and seven emission source categories in urban and rural environments. In accord with the Global Burden of Disease 2010 estimate 5.4 million deaths, Lelieveld et al calculate that outdoor air pollution, mostly by PM2.5, leads to 3.3 (95 per cent confidence interval 1.61-4.81) million premature deaths per year worldwide, predominantly in Asia. They primarily assume that all particles are equally toxic, but also include a sensitivity study that accounts for differential toxicity. They find that emissions from residential energy use such as heating and cooking, prevalent in India and China, have the largest impact on premature mortality globally, being even more dominant if carbonaceous particles are assumed to be most toxic. Whereas in much of the USA and in a few other countries emissions from traffic and power generation are important, in eastern USA, Europe, Russia and East Asia agricultural emissions make the largest relative contribution to PM2.5, with the estimate of overall health impact depending on assumptions regarding particle toxicity. Model projections based on a business-as-usual emission scenario indicate that the contribution of outdoor air pollution to premature mortality could double by 2050.

Question

What is the disease burden of fine particles and other major air pollutants globally?

Answer

In accord with the IHME global burden of disease for 2010 that estimated 5.4 million deaths, Lelieveld et al 2015 calculate that outdoor air pollution, mostly by PM2.5, leads to 3.3 (95 per cent confidence interval 1.61-4.81) million premature deaths per year worldwide, predominantly in Asia. They primarily assume that all particles are equally toxic, but also include a sensitivity study that accounts for differential toxicity. They find that emissions from residential energy use such as heating and cooking, prevalent in India and China, have the largest impact on premature mortality globally, being even more dominant if carbonaceous particles are assumed to be most toxic. Whereas in much of the USA and in a few other countries emissions from traffic and power generation are important, in eastern USA, Europe, Russia and East Asia agricultural emissions make the largest relative contribution to PM2.5, with the estimate of overall health impact depending on assumptions regarding particle toxicity. Model projections based on a business-as-usual emission scenario indicate that the contribution of outdoor air pollution to premature mortality could double by 2050.

+ Show code- Hide code #This code is Op_en7480/answer on page [[Disease burden of air pollution]] library(OpasnetUtils) gbd <- Ovariable("gbd", ddata = "Op_en7480", subset = "Attributable deaths due to air pollution") gbd <- EvalOutput(gbd) gbd <- oapply(gbd, cols = "Study", FUN = mean) oprint(summary(gbd))

Rationale

Interpretation

There are two global studies about disease burden of air pollution Lelieveld et al estimated for each 100 km * 100 km grid only numbers of attributable deaths (of which they used the term premature death). IHME institute produced both attributable deaths and DALYs for every country in the world. The exact details and assumptions are not very well documented, and therefore it is not clear from where the several-fold differences in country estimates come from. Probably the main differences are emission estimates and atmospheric transport modelling.

Both assessments were performed by highly respected researchers, and there is no easy way to determine, if one or another estimate is more likely. Therefore, as a default assumption, we assume that the truth can be either one and take the average. We can also say that the two estimates give a range within which each value is equally likely and thus form a uniform distribution. In both cases, the expected value is the same.

Data

Attributable deaths due to air pollution(#deaths/a)

Obs	Country	IHME GBD 2010	Lelieveld et al 2015
1	Global	5410949	3297000 (1610000-4810000)
2	China	1594207	1357000
3	India	1356579	645000
4	Pakistan	151882	111000
5	Bangladesh	148330	92000
6	Nigeria	94118	89000
7	Russia	119037	67000
8	United States	98529	55000
9	Indonesia	167863	52000
10	Ukraine	51280	51000
11	Vietnam	65331	44000
12	Egypt	37076	35000
13	Germany	41677	34000
14	Turkey	28586	32000
15	Iran	19814	26000
16	Japan	60971	25000
17	Poland	23846	15000
18	Ghana	17535	9000
19	Brazil	57176	<9000
20	Mexico	25538	<9000
21	South Africa	24423	<9000
22	Kenya	17250	<9000
23	Kazakhstan	13598	<9000
24	Angola	13182	<9000
25	Argentina	9972	<9000
26	Peru	8790	<9000
27	Cuba	2929	<9000
28	Australia	1418	<9000
29	Fiji	466	<9000

For details, see Lelieveld et al, Nature 2015 and Global burden of disease 2010 by IHME Institute

Lelieveld et al used a global atmospheric chemistry model to investigate the link between premature mortality and seven emission source categories in urban and rural environments.R↻

Lelieveld2015 disease burden methods

This section is a copy of Lelieveld et al 2015 study, from method section in the supporting online material. The usage of population attributable fraction is discussed and potential bias evaluated.

Exposure response functions. The premature mortality attributable to PM 2.5 and O 3 has been calculated by applying the EMAC model for the present (2010) and projected future (2025, 2050) concentrations. We combined the results with epi- demiological exposure response functions by employing the following relationship to estimate the excess (that is, premature) mortality (equation 1):R↻

$\Delta Mort = y_o[(RR-1)/RR]Pop$

ΔMort is a function of the baseline mortality rate due to a particular disease category y_o for countries and/or regions estimated by the World Health Organization 69 (the regions and strata are listed in the Extended Data Table 1). The term (RR 2 1)/RR is the attributable fraction and RR is the relative risk. The disease specific baseline mortality rates have been obtained from the WHO Health Statistics and Health Information System. The value of RR is calculated for the different disease categories attributed to PM 2.5 and O 3 for the population below 5 years of age (ALRI) and 30 years and older (IHD, CEV, COPD, LC) using exposure response functions from the 2010 GBD analysis of the WHO (and described below).

The population (Pop) data for regions, countries and urban areas have been obtained from the NASA Socioeconomic Data and Applications Center (SEDAC), hosted by the Columbia University Center for International Earth Science Information Network (CIESIN), available at a resolution of 2.59 3 2.59 (about 5 km 3 5 km) (http://sedac.ciesin.columbia.edu/), and projections by the United Nations Department of Economic and Social Affairs/Population Division 70 (http://esa.un.org/unpd/wpp). Urban areas are defined by applying a population density threshold of 400 individuals per km 2 , while for megacities and major conurbations the threshold is 2,000 individuals per km 2 . We note that the reso- lution of our atmospheric model, about 1u latitude/longitude, is coarser than that of the population data, and our model does not resolve details of the urban environment. However, our anthropogenic emission data are aggregated from a resolution of 10 km to that of the model grid, accounting for relevant details such as altitude dependence (for example, stack emissions and hot plume rise effects) 43 . Lelieveld et al. 21 (henceforth L2013) derived the relative risk RR from the fol- lowing exposure response function:

RR = exp[b(X-X<sub<o</sub>)] (2)

The term X represents the model calculated annual mean concentration of PM 2.5 or O3. The value of Xo is the threshold concentration below which no additional risk is assumed (concentration–response threshold). The parameter b is the concentration response coefficient. However, it has been argued that this expression is based on epidemiological cohort studies in the USA and Europe where annual mean PM 2.5 concentrations are typically below 30 mg m 23 , which may not be representative for countries where air pollution levels can be much higher, for example in South and East Asia. This is particularly relevant for our BaU scenario. Therefore, here we have used the revised exposure response function of Burnett et al. 8 who also included epidemiological data from the exposure to second-hand smoke, indoor air pollution and active smoking to account for high PM 2.5 concentrations, and tested eight different expressions. The best fit to the data was found for the following relationship, which was also used by Lim et al. 5 for the GBD for the year 2010:

RR = 1+a{1-exp{-b(x-Xo)^p]} (3)

The RR functions were derived by Burnett et al. 8 . We applied this model for the different categories, represented by their figures 1 and 2, shown to be superior to other forms previously used in burden assessments. We also adopted the upper and lower bounds, likewise shown in these figures, representing the 95% confidence intervals (CI95). The latter were derived based on Monte Carlo simulations, leading to 1,000 sets of coefficients and exposure response functions from which the upper and lower bounds were calculated. Following Burnett et al. 8 and Lim et al. 5 we combine all aerosol types, hence including natural particulates such as desert dust. Note that by using PM 2.5 mass, we do not distinguish the possibly different toxicity of various kinds of particles. This information is not available from epidemiological cohort studies, but could poten- tially substantially affect both our overall estimates of mortality and the geographical patterns. This is addressed by sensitivity calculations presented in the main text, Table 2 and Extended Data Fig. 1. For COPD related to O 3 we applied the exposure response function by Ostro et al. 3 :

RR = [(X+1)/(Xo+1)]^b

where b is 0.1521 and X o the average of the range 33.3–41.9 p.p.b.v. O 3 indicated by Lim et al. 5 , that is, 37.6 p.p.b.v. Previously we used model calculated pre-industrial O 3 concentrations to estimate X 2 X o (ref. 21), leading to about 20% higher estimates for mortality by ‘respiratory disease’ related solely to O 3 compared to the current estim- ate for COPD due to both PM 2.5 and O 3 .

For detailed discussion of uncertainties and sensitivity calculations that address the shape of exposure response functions, we refer to earlier work 5,8,21,22 and references therein. L2013 estimated statistical uncertainties by propagating the quantified (random) errors of all parameters in the exposure response functions. They found that the CI95 of estimated mortality attributable to air pollution in Europe, North and South America, South and East Asia are within 40%, whereas they are 100–170% in Africa and the Middle East. Our results are very close to the GBD, which substantiates the estimates by Lim et al. 5 and provides consistency with the most recent estimates for 2010, serving as a basis for our investigations.

We emphasize that the confidence intervals described here, and those reported by Lim et al. 5 , reflect only the statistical uncertainty of the parameters used in the concentration–response functions. It is known that the uncertainty in interpreta- tion of epidemiological results can be dominated by other model or epistemic uncertainties, such as those having to do with the control of confounders. Sources of uncertainty have been summarized by Kinney et al. 71 , who underscore the need to determine the differential toxicity of specific component species within the G 2015 complex mixture of particulate matter. Our sensitivity calculations (Table 2 and Extended Data Fig. 1) corroborate that this can have significant influence, espe- cially in areas where carbonaceous compounds contribute strongly to PM 2.5 .

We emphasize the dearth of studies that link PM 2.5 from biomass combustion emissions—rich in carbonaceous particles—to IHD. Expert judgment studies on the toxicity of particulate matter have reported uncertainties much larger than those suggested by analysis of parameter uncertainty alone 10,72 . Although the CI95 intervals provided above include a larger range of parameters and uncertainties than these earlier studies, they should be viewed as lower bounds on the true uncertainty in estimates of the health effects of PM 2.5 exposure, especially PM 2.5 from biomass burning and biofuel use. If we consider the possibility that biomass burning (BB, including agricultural waste burning) and residential energy use (RCO, dominated by biofuel use) do not contribute to mortality by IHD, the total mortality attributable to air pollution would decrease from 3.3 to 3.0 million per year (Extended Data Table 7). The largest effect is found in Southeast Asia where biomass combustion (RCO and BB) is a main source of air pollution. While the global contribution by residential energy use, as presented in Table 2, would decrease from 31% to 26%, and of biomass burning from 5% to 4% (the other categories increase proportionally), the ranking of the different sources and hence our conclusions remain unchanged, as RCO and BB would still be the largest and smallest source category, respectively.

Issues such as the shape of the concentration–response functions and the exist- ence and specific levels of concentration–response thresholds have been discussed by the experts 10,71,72 . These have been accounted for by Burnett et al. 8 , however, uncertainty related to the differences in central estimates given by various cohort studies is not reflected in the estimates of parameter uncertainty by Lim et al. 5 . This problem has grown more substantial recently as the results from new cohort studies have become available 73 . Furthermore, uncertainty about the relative toxicity of different constituents of PM 2.5 remains. Since the current study underscores that the sources of mortality attributable to PM 2.5 can differ strongly between different regions (Fig. 2), this aspect merits greater attention in future.

See also

• Disease burden of air pollution in Wikidata

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