Public health benefits of reducing exposure to ambient fine particulate matter in South Africa

Science of the Total Environment 684 (2019) 610–620

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Public health benefits of reducing exposure to ambient fine particulate matter in South Africa Katye E. Altieri a,b,⁎, Samantha L. Keen a a b

Energy Research Centre, University of Cape Town, Rondebosch 7700, South Africa Department of Oceanography, University of Cape Town, Rondebosch 7700, South Africa

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• The BenMAP model was applied to South Africa as a tool to systematize health impact analyses. • Fine particulate matter is a significant burden to public health in South Africa. • Reducing exposure to PM2.5 will avoid 28,000 premature deaths in South Africa. • The economic value of these avoided deaths is substantial, at $29.1 billion (2011$). • Local air quality, health data, and concentration response functions are critical for future work.

a r t i c l e

i n f o

Article history: Received 7 March 2019 Received in revised form 14 May 2019 Accepted 23 May 2019 Available online 25 May 2019 Editor: Pavlos Kassomenos Keywords: Air pollution Mortality Fine particulate matter BenMAP South Africa

a b s t r a c t Air pollution is a growing problem in developing countries, and there exists a wide range of evidence documenting the large health and productivity losses associated with high concentrations of pollutants. South Africa is a developing country with high levels of air pollution in some regions, and the costs of air pollution on human health and economic growth in South Africa are still uncertain. The environmental Benefits Mapping and Analysis Program (BenMAP) model was applied to South Africa using local data on population, mortality rates, and concentrations of fine particulate matter (PM2.5), as well as mortality risk coefficients from the epidemiological literature. BenMAP estimates the number of premature deaths that would likely have been avoided if South African air quality levels met the existing annual National Ambient Air Quality Standard (NAAQS) of 20 μg m−3, and the more stringent World Health Organization (WHO) guideline for annual average PM2.5 of 10 μg m−3. We estimate 14,000 avoided premature mortalities in 2012 if all of South Africa met the existing NAAQS annual average standard for PM2.5. These avoided cases of mortality have an estimated monetary value of $14.0 billion (US2011$), which is equivalent to 2.2% of South Africa's 2012 GDP (PPP, US2011$). We estimate 28,000 avoided premature mortalities if the more stringent WHO guideline for annual average PM2.5 is met across South Africa, which when expressed as a national burden is equivalent to 6% of all deaths in South Africa being attributable to PM2.5 exposure. These avoided cases of mortality have an estimated monetary value of $29.1 billion, which is equivalent to 4.5% of South Africa's 2012 GDP. These results show that there are significant public health benefits to lowering PM2.5 concentrations across South Africa, with correspondingly high economic benefits. © 2019 Elsevier B.V. All rights reserved.

⁎ Corresponding author at: Energy Research Centre, University of Cape Town, Rondebosch 7700, South Africa. E-mail address: [email protected] (K.E. Altieri).

https://doi.org/10.1016/j.scitotenv.2019.05.355 0048-9697/© 2019 Elsevier B.V. All rights reserved.

K.E. Altieri, S.L. Keen / Science of the Total Environment 684 (2019) 610–620

1. Introduction High concentrations of ambient particulate matter (PM) are associated with increased mortality risks, and there exists a wide range of evidence documenting the large health costs associated with high concentrations of air pollution (Pervin et al., 2008; Zhao et al., 2016). The Global Burden of Disease reported in 2012 that three million people die prematurely each year around the globe due to ambient air pollution (Brauer et al., 2012)), with low- and middle-income countries suffering the worst effects. Developing countries with a heavy reliance on fossil fuels, such as South Africa and China, face the bulk of the large health and productivity losses and mortality associated with high concentrations of air pollution. For China, which relies on coal for 75% of its primary energy, the economic burden of air pollution was estimated at 3.8% of their GDP in 2007 (The World Bank/State Environmental Protection Administration, 2007). This declined to 0.91% of GDP in 2016 as significant advances have been made to reduce PM2.5 levels over the last decade (Maji et al., 2018). The WHO estimates that air pollution costs European economies US$ 1.6 trillion a year in mortality and morbidity (WHO Regional Office for Europe OECD, 2015). South Africa is a middle-income country that relies on coal for 77% of primary energy (e.g., electricity generation, synthetic fuel production and petrochemical operations) and 93% of electricity supply (Department of Energy, 2010). In many regions of South Africa, PM is considered the pollutant of greatest concern (City of Cape Town, 2008; Thabethe et al., 2014). PM sources include fugitive dust, fires, mining, transportation, electricity generation, industrial activities, domestic fuel burning, and traffic, with the latter two sources identified as potentially the largest contributors to the PM-related burden (Feig et al., 2016; Venter et al., 2012). The South African PM10 (particulate matter b10 μm in diameter) 24-hour ambient standard was revised from 120 μg m−3 to 75 μg m−3 as of 2015, and previous work has shown the potential for multiple exceedances of this standard, especially in winter, industrialized areas (Venter et al., 2012), and lowincome areas (Hersey et al., 2015). Although secondary PM precursors are not the focus of this study, it is also important to note that the Vaal Triangle-Highveld region is the largest NO2 hotspot in the southern hemisphere due to the presence of 11 coal-fired power stations that are not fitted with technologies to reduce NO2 emissions, and this highlypopulated area accounts for 91% of South Africa's NOx emissions (Lourens et al., 2012; Wells et al., 1996). Similarly, there are large emissions of SO2 from industrial activities, coal mines, and power generation, particularly in winter (Feig et al., 2016; Lourens et al., 2011; Venter et al., 2012). These NOx and SO2 emissions inevitably contribute to additional PM formation and highlight the coupling between other pollutant emissions and the formation of secondary PM, which is typically in the PM2.5 size range (particulate matter b2.5 μm in diameter). In South Africa, cardiovascular diseases, respiratory diseases, and HIV/AIDS are three of the top five leading causes of death (Statistics South Africa, 2014). Pneumonia is the second leading natural cause of death in the over 15 age group and an important cause of mortality and morbidity in HIV infected adults (Cohen et al., 2015). The prevalence of HIV remains high in South Africa, at just under 17% in adults over 15 (Statistics South Africa, 2015). In 2011, 22% of South African households resided in shacks or traditional dwellings (Housing Development Agency, 2013). As such, even if the air pollution levels in South Africa were comparable to levels in more developed countries, the South African population may be more vulnerable to exposure, and more sensitive to the health effects of air pollution than populations in developed countries. The South African National Burden of Disease study quantified the relationship of indoor and urban outdoor air pollution to premature mortality in the year 2000. The annual average PM10 concentrations in 7 urban areas of South Africa were associated with 0.9% of all deaths in 2000 using global concentration response (C-R) functions (Norman et al., 2007b). In the same burden of disease study, indoor air pollution

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was found to cause fewer deaths than urban outdoor air pollution (0.5% of all deaths in South Africa in 2000), though the overall risk from indoor air pollution was ranked higher than that from urban outdoor air pollution (Norman et al., 2007a). Previous work on the disease burden of air pollution in South Africa has been limited by a lack of air quality monitoring data, as well as a lack of local, or even developing countryspecific, C-R functions (Norman et al., 2010; Scorgie and Watson, 2004; Wichmann, 2005). Here, we present an analysis of the estimated premature mortalities avoided and associated economic benefits of reducing PM2.5 concentrations in South Africa to the existing national standard and to the more stringent WHO guideline. This study fills the need for health impact assessments using local data at a spatial resolution that is meaningful towards establishing cost-benefit analyses of air pollution policies in South Africa. 2. Materials and methods The Environmental Benefits Mapping and Analysis Program (BenMAP) uses a damage-function approach to estimate the health benefits associated with a change in air quality (Davidson et al., 2007). BenMAP is a software package designed by the United States Environmental Protection Agency that systematizes the health impact and valuation calculation process. BenMAP relies on ambient concentration data of the pollutant of interest, user-specified population data, baseline incidence rates for the health endpoints of interest, epidemiological studies that provide C-R functions, and valuation functions (Fig. 1). Here, we estimate benefits from improvements in human health through reductions in relative mortality risk. The standard error of the mean in the epidemiological studies is perpetuated through the calculations using a Monte Carlo approach, which results in 95% confidence intervals around each mean health impact estimate. The model does not quantify benefits related to improved visibility or ecosystem effects. 2.1. South African data input to BenMAP The most recent official South African census data from 2011 were used to generate municipality-level population distribution grids in BenMAP (Statistics South Africa, 2015). For each municipality, the population is distributed by five race groups (i.e., Black African, Coloured, Indian or Asian, White, or Other), two genders (i.e., male and female), and ages in 5-year increments from 0 to 99. The South African Air Quality Information System (SAAQIS) was used to obtain air quality data for South Africa. The monitoring stations are controlled by a combination of private industry, local and provincial governments, the Department of Environmental Affairs, and the South African Weather Service, as such there is inconsistent overlap in the pollutants measured and the time frame of monitoring. In order to maximize spatial and temporal coverage, this study focuses on PM10 and particulate matter b2.5 μm in size (PM2.5) for the year 2012. The PM10 monitoring data set includes 60 stations within 7 provinces, while the PM2.5 monitoring data set includes 21 stations in 5 provinces (Figs. 2 and 3). The PM10 and PM2.5 data are reported hourly and processed by SAAQIS for quality assurance. Instruments at air quality monitoring stations are serviced and calibrated using NMISA certified calibration gases bi-weekly, and undergo a full calibration annually in a South African National Accreditation System accredited laboratory. SAAQIS data are recorded in real time and checked monthly for spikes and calibrated to adjust for any drift. These data were again quality checked (i.e., negative values, repeat values, and values of “999” were removed) and daily and annual averages were computed. Daily and annual averages were then loaded into BenMAP with the associated latitude and longitude values of the monitoring stations. Two air quality data sets were loaded into BenMAP for analysis. The first consisted of daily and annual averages from monitoring stations that measured PM2.5 directly

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StatsSA Census 2011 Gender, Race, Age by Local Municipality

SAAQIS 2012 Air Quality Monitoring Data Interpolated to Grid

PM2.5 C-R Functions: Krewski et al., 2009 Lepeule et al., 2012 Janssen et al., 2011 Burnett et al., 2018 Woodruff et al., 2006

SA BenMAP Inputs

Outputs

Population Estimates

Rollback to: NAAQS PM2.5 = 20 µg/m3 WHO PM2.5 = 10 µg/m3 Population Exposure

Adverse Health Effects

Baseline Incidence Rates StatsSA 2012 Mortality by District Municipality

Valuation Function SA VSL Economic Costs

Fig. 1. Schematic of the key data sources used as inputs to the South African BenMAP model (rectangles) and the model outputs at each step of the analysis (circles).

(Fig. 2). The second data set used province-specific PM2.5/PM10 ratios to improve geographical coverage (“PM2.5calc”). For stations that had concurrent PM10 and PM2.5 monitoring data (Fig. 2), daily ratios of PM2.5/

PM10 were calculated and averaged across the year for each province. The provincial PM2.5/PM10 ratios were then applied to monitoring stations within that province that only measured PM10. For example, in

Fig. 2. Locations of air quality monitoring stations that provided 2012 data for PM10 only (light grey stars), and both PM10 and PM2.5 (purple stars). The population is presented by municipality, highlighting that the air quality monitoring stations are co-located with areas of high population density. Provincial borders are denoted by solid black lines. Air quality priority areas are indicated by a white border, and are within the provinces of Mpumalanga, Gauteng, Free State, North West, and Limpopo. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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0

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Fig. 3. Daily mean (24-hour) concentrations of a) PM10 and b) PM2.5 presented as box and whiskers for every day measured in 2012. Individual stations are grouped by province (colours). The upper and lower part of the box is the 25th and 75th quartile, while the whiskers are the minimum and maximum values, excluding days which are considered statistical outliers. Outliers are calculated as daily means that are N3 times the upper quartile (circles). The median value is denoted by the solid line. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

2012 there were 5 stations in Gauteng with concurrent PM10 and PM2.5 data. The annual average of the daily PM2.5/PM10 ratios for the 5 stations was 0.59. This value was then applied to each PM10 24-hour average to calculate daily PM2.5 values for the other 19 Gauteng stations. This was done for each province such that the data set was expanded and “PM2.5calc” has the same geographical coverage as the PM10 data set. The PM2.5 and PM2.5calc air pollutant monitoring data were interpolated spatially to generate air quality grids using the Voronoi neighbour averaging (VNA) method. The VNA algorithm interpolates at each grid cell, in this case each municipality is a grid cell, utilizing the monitors nearest by drawing polygons around the center of each grid. An inverse-distance squared weighted average is used such that the farther the monitor is from the grid cell, the smaller the weight its value is given. South African mortality statistics (Statistics South Africa, 2014) were used with the census data to calculate the baseline incidence rate for allcause mortality in each district municipality, which is the second level of administrative division in South Africa, above local municipalities. The mortality statistics are collated from civil registrations on mortality and causes of death from the South African Department of Home Affairs for 2012. Causes of death statistics are in accordance with the WHO regulations and use the International Classification of Diseases (ICD-10) (WHO, 2009). 2.2. Health impact function The health impact function is determined by the chosen epidemiological study. For PM2.5 we rely on studies conducted in the USA and

utilized by the WHO for the GBD (Krewski et al., 2009; Lepeule et al., 2012; Woodruff et al., 2006), as well as a recent meta-analysis that includes European and American studies (Janssen et al., 2011), and the more recent Global Exposure Mortality Model (Burnett et al., 2018). This type of analysis is a critical component of air quality policy and management planning that could be further developed and systematized in South Africa. Ideally, a concentration-response function would be generated using local populations exposed to PM2.5 of similar levels and with a similar chemical composition, however, there are no South African specific studies to utilize in this case. The Krewski et al. (2009) study is a reanalysis of the American Cancer Society (ACS) study (Pope III et al., 2002), which controls for 44 individual and 7 ecological covariates based on exposure in 116 cities in the United States. The ACS study includes a large population and broad geographical area, therefore even if similar PM2.5 levels are experienced it is likely that the chemical composition varies. For example, there is evidence that high amounts of black carbon present in PM2.5 leads to larger hazard ratios for a given concentration of PM2.5 (Janssen et al., 2011). However, the population of the ACS study is more affluent than the USA national average, and is not a racially diverse population, such that the exposure patterns may be quite different than for a South African population. The second USA based study we applied is an extended reanalysis (Lepeule et al., 2012) of the Harvard Six Cities (H6C) study, which is more racially and economically representative than the ACS study, but is focused on six cities in the eastern part of the USA where PM chemical composition is likely to be more

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homogeneous. However, this area has a higher sulphate fraction than the western part of the USA, which is more similar to the highsulphate PM2.5 found in polluted regions of South Africa. Annual average PM2.5 concentrations in the ACS study range from 5 to 50 μg m−3 (Krewski et al., 2009) and in the H6C study range from 10 to 40 μg m−3 (Lepeule et al., 2012), which are comparable to South African air quality values (Section 3.1 below). The Global Exposure Mortality Model is based on data from 41 cohort studies across 16 countries spanning the full global exposure range of PM2.5 concentrations (up to 87 μg m−3) (Burnett et al., 2018). For comparison to other South African health impact assessments, we also use a systematic review and metaanalysis based on time-series and cohort studies of adult populations from the USA and Europe (Janssen et al., 2011). To account for the heterogeneity in the individual risk estimates, we use the BenMAP random effects pooling to combine the distributions of each study and to generate a single mean relative risk estimate derived from the four chosen epidemiological studies. The pooling of HIFs is feasible in this case as all of the studies have the same health endpoint, however, it is important to note that the studies use slightly different ages for the lower end of the population range (i.e., 25–30 years old). BenMAP assigned relative weightings to each of the four studies based on the spread of each individual study and how it differed from the others pooled. The Krewski and Burnett studies were assigned weightings of 0.319 each, while the Janssen and Lepeule studies were assigned weightings of 0.218 and 0.142, respectively. The four chosen studies provide a dose-response function for annual exposure concentration values relying on the Cox proportional hazards model and thus the log-linear health impact functional form (Eq. (1)). The health impact functional form is used to determine the relationship between the change in mean annual PM2.5 concentrations (Δx) and the corresponding change in excess all-cause mortality (Δy) for the given population (pop)
1 Δy ¼ y0 1− βΔx Þ  pop e

ð1Þ

where y0 is the baseline incidence rate for the health endpoint and β is the coefficient calculated from the odds ratio that is measured in the epidemiological study. These studies focus on adult populations, however, there is also evidence for post neonatal infant mortality associated with PM2.5 (Woodruff et al., 2006), which relies on the conditional logistic regression model (Eq. (2)).
1 Þ  pop Δy ¼ y0 1− ð1−y0 ÞeβΔx þ y0

ð2Þ

where y0 is the baseline incidence rate for the health endpoint and β is the coefficient calculated from the odds ratio that is measured in the epidemiological study. The % excess relative risk and interquartile ranges (IQR) were used to determine the underlying coefficient (β) and its associated standard deviation (σβ) for use in the health impact function. 2.3. Valuation There is a broad economics literature on potential methods for valuing reductions in premature mortality risks. The majority of this literature focuses on North America and Western Europe, and even with the large number of studies there still remains a debate within the economics and public policy communities on the appropriate way to approach this valuation. In developing countries, there is limited information on the suitable discount rates, importance of age and quality of life, and the overall willingness-to-pay (WTP) for reduced mortality. The WTP represents the economic value that individuals are willing to trade for a relative reduction in mortality risk. The value transfer method is thus frequently applied to determine developing country value of a statistical life (VSL) estimates (World Bank and Institute for

Health Metrics and Evaluation, 2016; Yaduma et al., 2013). In this case, we follow the World Bank (2016) study on the cost of air pollution and use the OECD specified VSL of $3.8 M (2011 $) (World Bank and Institute for Health Metrics and Evaluation, 2016, which can be used for any country as the basis for a value transfer calibration. The VSL for South Africa is calculated using Eq. (3), 

e capSouth Africa Þ  GDP capOECD

GDP

VSLSouth Africa ¼ VSLOECD 

ð3Þ

where GDP/cap is the purchasing power parity (PPP) of the GDP percapita (data.worldbank.org/country) and e is the elasticity of the WTP for a marginal reduction in mortality risk with respect to income. The VSL for South Africa is calculated using elasticities of 1, 1.2, and 1.4 as recommended by the World Bank (2016) to account for the large income disparity between South Africa and other developed countries in the OECD. The resulting VSL values were input to BenMAP as a triangular distribution and used to calculate the costs of premature mortality associated with exposure to ambient PM2.5 using a Monte Carlo approach. 3. Results 3.1. Air quality levels The provinces of Gauteng and Mpumalanga have a large number of air quality monitoring stations as they are areas with high population densities, and two of the three government designated Air Pollution Priority Areas fall primarily within these provinces (Fig. 2). Daily average PM values vary spatially across all provinces and across the year (Fig. 3). Stations with consistently high PM concentrations typically occur near areas with high levels of domestic combustion, near highly industrialized areas (e.g., Secunda in Mpumalanga), and near coal yards and coal burning power stations, including previously mothballed plants that were re-commissioned due to power shortages, and others that have had their operating lives extended and still use older technology (Feig et al., 2016; Kuik et al., 2015; Venter et al., 2012). Across the 21 stations where PM2.5 was measured directly in 2012 (Fig. 3b), the 24hour average NAAQS PM2.5 standard (40 μg m−3) was exceeded on 18% of the days and the WHO 24-hour average PM2.5 guideline (25 μg m−3) was exceeded on 40% of the days. The mortality associated with PM2.5 is typically associated with chronic exposure (Kunzli et al., 2001), thus the annual average is utilized in the BenMAP analyses here. The annual average calculated for each station where PM2.5 is monitored ranged from 4.9 to 43.3 μg m−3 with an average of 24.1 μg m−3 (n = 21 stations). The NAAQS for annual average PM2.5 is 20 μg m−3, which was exceeded at 13 of the 21 stations in 2012. The annual average PM2.5 WHO guideline is 10 μg m−3, which was exceeded by all stations except one in 2012. The annual averages calculated here for the Highveld and Vaal Triangle Priority Area stations are within 1% of the values reported in Garland et al. (2017) and are consistent with online published reports (https://saaqis.environment.gov. za). The annual average for each station in the PM2.5calc data set, i.e., including the PM2.5 calculated from PM2.5/PM10 ratios, ranges from 4.9 to 64.3 μg m−3 with an average of 25.9 μg m−3 (n = 60 stations). The air quality monitoring data sets were interpolated using the VNA method to generate maps of annual mean PM2.5 and PM2.5calc values for each South African municipality. After interpolation, the annual mean PM2.5 values for all South African municipalities ranged from 7.7 to 42.1 μg m−3 with a mean of 22.0 μg m−3, while the PM2.5calc values range from 4.2 to 49.0 μg m−3 with a mean of 18.1 μg m−3. These are the baseline air quality grids used in the BenMAP analysis. To calculate the change in excess mortalities using Eq. (1), the baseline air quality grids are compared to two control grids. Air quality stations in the monitoring data set with values above the standard (e.g., NAAQS or WHO

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Table 1 Reductions in all-cause premature mortality associated with reducing 2012 annual average PM2.5 values to the NAAQS and WHO guidelines. The average (95% confidence interval) for individual concentration-response functions, and the pooled health impact function are presented. PM2.5 – chronic exposure premature mortality (all cause)

Mortalities avoided by attaining NAAQS PM2.5 2012

Krewski et al. (age N 30)

Mortalities avoided by attaining NAAQS PM2.5Calc 2012

9000 (6000–12,000) 20,000 (10,000–30,000) 18,000 (12,000–25,000) 10,000 (7000–13,000) 14,000 (7000–26,000) 500 (−600–1500)

Lepeule et al. (age N 25) Janssen et al. (adult meta-analysis) Burnett et al. (age N 25 GEMM) Pooled Across HIFs Woodruff et al. (age b 1 year)

Mortalities avoided by attaining WHO Guideline PM2.5 2012

8000 (5000–10,000) 17,000 (9000–25,000) 16,000 (10,000–21,000) 8000 (6000–11,000) 11,000 (6000–21,000) 400 (−500–1150)

19,000 (13,000–25,000) 43,000 (22,000–61,000) 39,000 (25,000–52,000) 23,000 (17,000–29,000) 28,000 (15,000–52,000) 1000 (−1200–2800)

Note: For presentation purposes, the results were rounded.

guideline) are reduced to the standard value, leaving monitoring stations with a value at or below the standard at their original value. The adjusted monitoring data set is then interpolated using the VNA method to generate a control grid. 3.2. National benefits The benefits of achieving the NAAQS annual average PM2.5 standard everywhere in South Africa are presented as estimated avoided mortalities (Table 1). There are 9000 (95% CI: 6000–12,000) estimated PM2.515°E

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related avoided premature all-cause mortalities from 2012 air quality levels using the ACS reanalysis (Krewski et al., 2009), 20,000 (10,000–30,000) using the H6C reanalysis (Lepeule et al., 2012), 18,000 (12,000–25,000) using the Janssen meta-analysis (Janssen et al., 2011), and 10,000 (7000–13,000) using the GEMM metaanalysis. In addition, there are 500 (−600–1500) estimated PM2.5related infant deaths from 2012 air quality levels predicted using a study conducted in California (Woodruff et al., 2006). The large variability in the calculated avoided mortalities across health impact functions highlights the importance of choosing appropriate C-R functions for

City of Tshwane City of Johannesburg Ekurhuleni

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Fig. 4. Avoided PM-related premature mortality estimated for 2012 if annual NAAQS were met across South Africa. Mortalities were calculated for each municipality using the pooled C-R functions and then summed across municipalities to the district municipality level. a) Total estimated premature mortality and b) population normalized values are presented. The top five local municipalities most impacted by the reduction in air pollution to the NAAQS and WHO levels are labelled on panel a. Air quality priority areas are indicated by a white border, South African district municipalities by a grey border, and South African provinces by a solid black line.

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this type of analysis. As discussed in Section 2.2, there are limitations to each C-R function, but there is broad and consistent support in the literature for a very strong relationship between long-term exposure to PM2.5 and all-cause mortality in many countries, across the entire range of PM2.5 concentrations, and for PM2.5 of varying compositions. Here, we use a pooled estimate of the true C-R function by combining estimates of the pollutant coefficient (β) and their standard deviation (σβ) for each of the four studies discussed above to determine a pooled HIF. The PM2.5Calc premature all-cause mortalities avoided by meeting the NAAQS were very similar in number to the PM2.5 monitor data (Table 1). The majority of the PM2.5 monitoring stations are co-located with areas of high population density (Fig. 2), thus the addition of the PM2.5calc data had a minor impact on the overall mortality calculation as it expanded the data set in regions of lower population density. The distributions of all-cause mortality for the PM2.5 and PM2.5Calc data sets are not statistically different (pooled HIF; Kolmogorov-Smirnov nonparametric test of the equality of one-dimensional probability distributions, two-sided, p-value = 0.175, D = 0.35), and from this point forward we present results from the PM2.5 monitoring data using the pooled HIF only. If all of South Africa met the annual average PM2.5 NAAQS of 20 μg m−3 in 2012, there would have been an estimated 14,000 (95% CI: 7000–26,000) premature all-cause mortalities avoided in that year. When compared to the baseline mortality rate for South Africa, meeting the NAAQS would result in a 4.2% decrease in all-cause mortality nationally. If South Africa met the more stringent annual average WHO guideline of 10 μg m−3, there would have been an additional 14,000 mortalities avoided. The total premature all-cause mortalities avoided based on the change from 2012 air quality levels to the WHO guideline

is estimated at 28,000 (95% CI: 15,000–52,000). Meeting the WHO guidelines for annual average PM2.5 in 2012 would result in an 8.8% decrease in all-cause mortality nationally. 3.3. Regional benefits The mortality impacts are calculated for each municipality. These municipal level impacts are then summed to the level of district municipality and presented as maps to illustrate the spatial distribution of the benefits to reduced PM2.5 concentrations (Figs. 4 and 5). The spatial distribution of PM2.5-related avoided mortalities shows larger values near densely populated areas, within air quality priority areas, and in regions with high pollution levels (Figs. 4a and 5a). In general, the total mortalities avoided for a given area are driven by some combination of population size, the change in air pollution to meet the standard (known as the delta concentration value), and the baseline mortality rate (pop, Δx, and y0, respectively in Eqs. (1) and (2)). The spatial distribution of population-normalized mortalities avoided eliminates the influence of population and is dependent only on the delta concentration value, and the baseline mortality rate (Figs. 4b and 5b). Large delta concentration values are also associated with a high percentage of all-cause mortalities that could be avoided by air pollution reductions. The ten municipalities with the largest numbers of mortalities avoided are presented in Table 2, and the top five municipalities are labelled on Fig. 4a. The largest number of mortalities avoided is in Ekurhuleni, a highly urbanised area located in the Highveld Priority Area where O.R. Tambo International airport is located. Ekurhuleni has the sixth largest delta concentration value and the fourth largest population of all municipalities. Eight of the ten municipalities in Table 2 are in the top ten in terms of population size, highlighting that large

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Fig. 5. Avoided PM-related premature mortality estimated for 2012 if annual WHO guidelines were met across South Africa. Mortalities were calculated for each municipality using the pooled C-R functions and then summed across municipalities to the district municipality level. a) Total estimated premature mortality and b) population normalized values are presented. Air quality priority areas are indicated by a white border, South African district municipalities by a grey border, and South African provinces by a solid black line.

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Table 2 Reduction in premature mortality associated with achieving annual NAAQS standard and WHO guidelines for the most impacted local municipalities using the pooled HIF. The numbers in the first two columns can be summed to the last column, which is the total number of mortalities avoided by decreasing annual average PM2.5 from 2012 values to the WHO guideline. Local municipality (province) Ekurhulenia (Gauteng) City of Johannesburgb (Gauteng) City of Tshwane (Gauteng) Emfulenib (Gauteng) eThekwini (KwaZulu-Natal) Mangaung (Free State) Buffalo City (Eastern Cape) Matjhabeng (Free State) City of Matlosana (North West) City of Cape Town (Western Cape)

Mortalities avoided by attaining annual Additional mortalities avoided by going from NAAQS standard Mortalities avoided by attaining NAAQS standard to WHO guideline WHO guideline 1410 1140 720 590 560 370 300 220 200 0

1190 1360 930 370 460 310 180 170 180 1360

2600 2500 1650 960 1020 680 480 390 380 1360

Note: For presentation purposes, the results were rounded. a Located in the Highveld Priority Area. b Located in the Vaal Triangle Priority Area.

populations are a driver of large premature deaths. For example, the cities of Johannesburg, Cape Town, Durban (eThekwini municipality), Ekurhuleni, and Pretoria (City of Tshwane municipality) are the five largest cities in South Africa, and East London (Buffalo City municipality) and Bloemfontein (Mangaung municipality) are the seventh and eighth largest cities, respectively. Of these large metropolitan municipalities, the City of Cape Town is the only area where PM2.5 is on average already below the NAAQS annual standard of 20 μg m−3 and no mortalities are avoided until air pollution levels are reduced to the WHO guideline (Table 2 and Fig. 5). The spatial pattern of PM2.5-related mortalities avoided by reducing to the WHO guideline of 10 μg m−3 is similar to the pattern from reductions to the NAAQS. There is a large increase in the mortalities avoided in the City of Cape Town, which has a large population and annual average PM2.5 just at or below the NAAQS (Table 2). In addition, the three air quality priority areas are significantly impacted by the additional reductions, discussed below in Section 3.3.1 (Fig. 5). 3.3.1. Air pollution priority areas The Vaal Triangle Airshed was declared the first national air pollution priority area in South Africa in 2006 (Department of Environmental Affairs, 2006). It is a region with 3.95 million people and intense urbanisation and industrial activity including mining, coal fired power generation, a coal-to-liquids plant, iron and steel processing, and domestic fuel burning. There are six air quality monitoring stations within the Vaal Triangle Airshed Priority Area (VTAPA), and in 2012 all six stations had annual average PM2.5 concentrations that exceeded the NAAQS with values ranging from 27.0 to 39.6 μg m−3. If annual average PM2.5 within the VTAPA met the NAAQS, 1850 (1000–3550) premature mortalities would be avoided annually. This would be a 6.6% decline in mortality within the VTAPA. If further reductions were made such that the WHO guideline was met at all locations within the VTAPA, an additional 1880 (990–3210) mortalities would be avoided. Meeting the WHO guideline for annual average PM2.5 would result in a 13.3% decrease in all-cause mortality. In 2007, the Highveld Priority Area (HPA) was declared the second national air pollution priority area in South Africa (Department of Environmental Affairs, 2006). The Highveld has a population of 3.4 million people, and is a large industrial and agricultural area with mining, metallurgical, and petrochemical activities, coal-fired power stations, coal dumps, and urban areas with domestic fuel burning and biomass burning (Lourens et al., 2011). There are five air quality monitoring stations within the HPA, and in 2012 four of the five monitoring stations had annual average PM2.5 above the NAAQS, with values ranging from 18.3 to 27.0 μg m−3. If annual average PM2.5 within the HPA met the NAAQS in 2012, 1670 (900–3200) premature mortalities would be avoided. This would be a 6.8% reduction in all-cause mortality within the HPA. If further reductions were made such that the WHO guideline

was met, an additional 1540 (850–2800) mortalities would be avoided. In total, meeting the WHO guideline would result in a 12.1% decrease in all-cause mortality in the HPA. 3.4. Valuation BenMAP uses the distributions of pooled mortalities and the VSL for South Africa to calculate a distribution of monetary costs associated with the PM burden. The relative reduction in mortality risk associated with meeting the NAAQS everywhere in South Africa have an estimated monetary value of $14.0 billion ($7.3–$27.4 billion; US2011$), which is equivalent to 2.2% of South Africa's 2012 GDP (1.1%–4.2%; World Bank PPP US2011$). Meeting the WHO guidelines increases the monetary value to $29.1 billion ($15.1–$55.3 billion), which is equivalent to 4.5% of South Africa's 2012 GDP (2.3%–8.5%). The monetary benefit of meeting the NAAQS, and the additional benefit of meeting the WHO guideline can be calculated for each province (Fig. 6). In both cases the monetary value is by far the highest for Gauteng, although Gauteng has the highest GDP of all South African provinces (StatsSA) such that the percentage of GDP is not the highest. The largest impacts as a fraction of provincial GDP are for the Northern Cape, Eastern Cape and the Free State. 4. Discussion 4.1. Uncertainty analysis There are many sources of uncertainty that lead to a range of estimates for the PM-related mortality burden across South Africa. The 95% confidence intervals reported here are based solely on the uncertainty in the concentration-response function as reported in the original epidemiological studies (Davidson et al., 2007; Fann et al., 2011; Sacks et al., 2018). In calculating the total estimated number of premature mortalities that could be avoided due to reductions in air quality to the NAAQS and WHO guidelines, there are other sources of uncertainty that are not explicitly included in these confidence intervals. The calculations are conducted for each municipality across the country, yet air quality monitoring stations cover limited geographical areas (Fig. 2). The air quality grid is created via an inverse distance squaredweighted interpolation, which introduces error for the areas farthest away from monitoring stations. This error is minimized for the national analysis as monitoring stations are located near large population centres, thus the municipalities with uncertain air quality values are more likely to have low populations and will contribute little to the overall mortality burden. However, it is important to note that some of these areas may have high PM2.5 levels due to local industry and domestic fuel use. As such, the national values presented here for premature mortalities avoided are likely to be underestimates of the actual national

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$12,000

14

Millions USD (2011$)

$10,000

WHO 12

Populaon

4.1%

10

$8,000

8 $6,000 $4,000

2.1%

6.9%

7.1%

7.2%

$2,000

3.9%

4.7%

3.9%

FS

GT

KZN

2.7%

4.0%

2.1%

$0

EC

6

4.0%

2

7.8% 3.5%

2.1%

1.2%

LIM

MP

NW

4

Millions of People

NAAQS

3.9%

0.1%

NC

WC

0

Fig. 6. The monetary value (in 2011$) of the avoided premature mortalities associated with meeting NAAQS (dark grey bars) and the more stringent WHO guidelines (light grey bars) are summed across municipalities to determine a total for each province (left axis). The costs as a percentage of 2012 provincial GDP (in 2011$) are labelled on each bar. The total population for each province is also presented (black circles; right axis). EC = Eastern Cape, FS = Free State, GT = Gauteng, KZN = KwaZulu-Natal, LIM = Limpopo, MP = Mpumalanga, NW = North West, NC = Northern Cape, WC = Western Cape.

burden. The error associated with a lack of monitoring stations will be more important for the regional and spatial analysis. When normalized to population (Figs. 4b and 5b), regions far from monitoring stations are highlighted as potentially having large per-capita impacts. These regions should be targeted for future air quality monitoring stations to evaluate the actual exposure to fine PM. A reliance on sparse monitoring is necessary due to the absence of highly resolved air quality modelling data for South Africa. This highlights the importance of both improving the sparse air quality monitoring network, and removing barriers to high resolution air quality modelling (e.g., highly resolved emissions inventories). While there are a plethora of epidemiological studies that quantify the relationship between mortality and morbidity due to air pollution in North America and Europe (Atkinson et al., 2011; Pope et al., 1995; van der Kamp and Bachmann, 2015), there is a dearth of similar data for developing countries in Africa. Local differences in the composition of PM, building characteristics, time-activity and exposure patterns, and especially the vulnerability of local populations due to factors that inhibit immune system function for example high rates of TB and HIV, suggest that using developed country epidemiology studies may result in large uncertainties in the estimated benefits of air pollution reductions in South Africa (Wichmann, 2005). 4.2. Economic benefit valuation Air pollution leads to a variety of economic costs related to ecosystem damage, visibility of natural areas, loss of work days, and human health impacts including both morbidity and mortality. The largest economic cost of air pollution is consistently found to be the cost of premature mortality (World Bank and Institute for Health Metrics and Evaluation, 2016), and the large majority of economic benefits due to reductions in air pollution are the avoided premature mortalities (Berman et al., 2012; Fann et al., 2012). Therefore, here we focus on quantifying the economic benefits associated with the reduction in premature mortality due to reducing air pollution in South Africa to a national standard and an international guideline. The most commonly used method to value the full economic costs of air pollution related mortality is the welfare-based approach known as WTP. When summed over many individuals' WTP, the VSL is determined. It is important to note that the VSL is not a metric meant to represent the value of a single person's life or the societal value of a person's life. Ideally, a WTP survey conducted in South Africa would be used to determine a VSL for South Africa, but that is well beyond the scope of this study. Here, we use the commonly accepted benefit-transfer approach of adjusting a base VSL to the South African context and we allow for a variety of income

elasticities to represent the differing WTP for individuals with varying incomes ((Narain and Sall, 2016) and references therein). The benefit-transfer approach has been used to estimate a VSL for South Africa in a number of international VSL studies. Hammitt and Robinson (2011) estimate a VSL range of $0.3 million to $1.4 million (2011$) for South Africa using an elasticity range of 2 to 1, respectively (Hammitt and Robinson, 2011). Viscusi and Aldy (2003) estimate a VSL for South Africa of $0.99 million (2011$) using an income elasticity of 1, and they recommend a value of $1.1 million (2011$) for upper-middle income country's in general (Viscusi and Aldy, 2003). Miller (2000) estimates a range of VSL values for South Africa from $0.59 million to $1.2 million (2011$) using an elasticity of 1 (Miller, 2000). Here, we follow the recommendations for developing countries from a recent World Bank study on the cost of air pollution and use an elasticity of 1.2 to generate the mean VSL estimate, with an elasticity range of 1 to 1.4 for sensitivity analysis (World Bank and Institute for Health Metrics and Evaluation, 2016). The resulting mean VSL is $1.03 million, with a range from $0.8 to $1.3 million (2011$). The estimated VSL of $1.3 million (2011$) when using an elasticity of 1 is comparable to the estimates discussed above where an elasticity of 1 is used. The range in values is not chosen to represent income disparity in South Africa, but the uncertainty in assigning a VSL using the value transfer method. A more appropriate VSL would result from a contingent valuation study that included South African participants from across a range of household incomes and socioeconomic levels. Using a range of VSL values from $0.8 to $1.3 million results in a range of monetary benefits estimated, as such we report the average and 95% confidence interval of the benefits at all times. 4.3. Comparison to other studies of South Africa The GBD model estimates a median population weighted PM2.5 level of 27 μg m−3 for South Africa, with a range of 18 to 42 μg m−3. They estimate 14,356 total deaths using a control value slightly lower than the WHO guideline of 10 μg m−3 (World Health Organization, 2016). The upper and lower range of PM2.5 concentrations is used to calculate a mortality range of 11,397 to 17,206 (World Health Organization, 2016). The median PM2.5 concentration used by the GBD study is comparable to the average that we estimate across all of South Africa, although the GBD model does not capture the areas with lower PM2.5 levels. The GBD study calculates mortality for five diseases and sums them, whereas here all-cause mortality is calculated, therefore a direct comparison is difficult. A World Bank study on the economic costs of air pollution estimated that South Africa has an annual average PM2.5 of 14.33 μg m−3 and 19,802 deaths due to air pollution in 2013 (World

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Bank and Institute for Health Metrics and Evaluation, 2016). They calculate the total welfare losses of these mortalities to be $20.66 billion (2011$) and 3.12% of GDP. In contrast to the studies discussed above, we calculate a significantly larger number of excess mortalities due to chronic exposure to PM2.5. This discrepancy is likely caused by the spatial resolution of the analyses, as the GBD and World Bank estimates are based on nationallevel analysis, i.e., total population times the PM2.5 value, whereas BenMAP computes excess mortality in each grid cell using higher resolution input data (e.g., incidence by district municipality, population and PM2.5 concentration by local municipality). Thus, local differences in air pollution, population, and mortality incidence rates are reflected in the BenMAP analysis and propagated through when the values are summed to the national level. The critical factor in the more local scale analysis presented here is that the higher air pollution levels occur in high population areas. A national scale analysis does not account for that type of spatial heterogeneity. The World Bank study is comparable in design to this study, but they also take a national approach and do one calculation for all of South Africa. As a result, the low estimate of annual average PM2.5 leads to an under-estimate of the total mortalities, which is propagated to an under-estimate of the total economic costs. The under-estimate by global assessments of PM2.5 concentrations in South Africa is common as analysis by (Garland et al., 2017) showed that global assessments of South Africa based on satellites or global modelling under-estimated annual average PM2.5 in priority areas by ~3.7 fold. The global mean per capita mortality rate due to air pollution exposure is estimated at 120 deaths per year per 100,000 people (Lelieveld et al., 2019). There are large areas of South Africa that are comparable to or exceed this global average mortality rate (Fig. 5b), highlighting the importance of improving South African air quality. A South African burden of disease study conducted in 2000 focused on mortality associated with urban air quality in 6 metropolitan areas. The population weighted annual mean PM2.5 estimated from PM10 measurements and PM10/PM2.5 ratios across the urban areas was 26.6 μg m−3 (Norman et al., 2010), which is very similar to the GBD study and comparable to the measured PM2.5 in this study. Norman et al. (2010) focused on the 5.5 million people living within 5 km of urban centers and three diseases using the ACS study C-R functions, with a control value slightly lower than the WHO guideline of 10 μg m−3. They estimate 4637 deaths with a range of 1480 to 7838 in the year 2000 due to chronic PM2.5 exposure. Here, the total mortalities in the six largest urban areas sum to 11,500 deaths, which is more than double the South African disease burden estimate cited above. 5. Conclusions Burden of disease studies that use one national-level calculation and inputs from global models or satellites are useful for a rough estimate of South Africa's pollution related mortality burden. However, they tend to significantly under-estimate the total health burden of air pollution exposure, and are incapable of capturing regional heterogeneity. As a result, these international studies are not helpful in motivating for or designing policies for reducing air pollution. Air quality management in South Africa is conducted across and between the three autonomous spheres of government, i.e., the national, provincial, and local levels. As such, information on local air pollution, and the mortality and monetary benefits of reducing air pollutants is required at the city, municipal, provincial, and national levels. In addition, analyses focused on the crossboundary Air Pollution Priority Areas are necessary for evaluating policies specific to these regions. This study has highlighted that BenMAP is a useful tool for assisting in spatial analyses of air quality policy benefits. Overall, there would be significant reductions in mortality if the NAAQS and WHO guidelines were met across South Africa. There is also a large economic cost associated with these mortalities. Improved access to local scale air quality and health data would improve the

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