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Dust pollution from the Sahara and African infant mortality

Nature Sustainability volume 3, pages 863–871 (2020)Cite this article

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Abstract

Estimation of pollution impacts on health is critical for guiding policy to improve health outcomes. Estimation is challenging, however, because economic activity can worsen pollution but also independently improve health outcomes, confounding pollution–health estimates. We leverage variation in exposure to local particulate matter of diameter <2.5 μm (PM2.5) across Sub-Saharan Africa driven by distant dust export from the Sahara, a source uncorrelated with local economic activity. Combining data on a million births with local-level estimates of aerosol particulate matter, we find that an increase of 10 μg m–3 in local annual mean PM2.5 concentrations causes a 24% increase in infant mortality across our sample (95% confidence interval: 10–35%), similar to estimates from wealthier countries. We show that future climate change driven changes in Saharan rainfall—a control on dust export—could generate large child health impacts, and that seemingly exotic proposals to pump and apply groundwater to Saharan locations to reduce dust emission could be cost competitive with leading child health interventions.

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Fig. 1: Local air pollution in Africa is driven by both local economic activity and remote natural sources.
Fig. 2: Variation in dust and rainfall over the Bodélé Depression are strong predictors of variation in PM2.5 concentrations elsewhere in Africa.
Fig. 3: Instrumental variables (IV) estimates suggest large impacts of PM2.5 on infant mortality, with effect sizes similar to quasi-experimental studies from higher income countries.
Fig. 4: Disagreement among climate model projections of future rainfall changes in the Sahara generates a large spread in projected infant mortality changes in Africa.
Fig. 5: Cost per averted life-year lost under an intervention using pumped groundwater to reduce dust export from the Bodélé.

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Data availability

Data are available at https://github.com/burke-lab/NatSus2020.

Code availability

Code to replicate all results is available at https://github.com/burke-lab/NatSus2020.

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Acknowledgements

We thank the National Science Foundation (CNH award no. 1715557) and the Robert Wood Johnson Foundation for funding. We also thank M. Greenstone for motivating comments on an earlier presentation.

Author information

Authors and Affiliations

  1. Center on Food Security and the Environment, Stanford University, Stanford, CA, USA

    Sam Heft-Neal & Marshall Burke

  2. School of Global Policy and Strategy, UC San Diego, La Jolla, CA, USA

    Jennifer Burney

  3. School of Medicine, Stanford University, Stanford, CA, USA

    Eran Bendavid

  4. Scripps Institution of Oceanography, UC San Diego, La Jolla, CA, USA

    Kara K. Voss

  5. Department of Earth System Science, Stanford University, Stanford, CA, USA

    Marshall Burke

  6. National Bureau of Economic Research, Cambridge, MA, USA

    Marshall Burke

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  1. Sam Heft-Neal
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Contributions

M.B. and S.H.-N. conceived of the paper. S.H.-N., J.B., K.K.V. and M.B. analysed the data. All authors contributed to interpreting the results and writing the paper.

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Correspondence to Marshall Burke.

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Extended data

Extended Data Fig. 1 Location of observed births and their distance to the Bodélé Depression.

(a) Each point represents one of the 28,461 DHS clusters included in our sample. The number of observed births in a single cluster ranges from 1 to 210. Orange points indicate the West Africa sample. The full sample includes all points. The Bodélé Depression is outlined in black. (b) Observed births are 500-5000km away from the Bodélé Depression.

Extended Data Fig. 2 Dust is rapidly transported from the Bodélé Depression across West Africa and beyond and correlation of local dust concentration with Bodélé dust dissipates with distance.

(a) Daily dust propagation, measured as Dust Aerosol Optical Depth (DAOD), over eight days during a large dust activation event illustrates the magnitude of dust concentrations as well as the timescale and spatial extent of transmission. Dust AOD ranges from 0 to greater than 3.0 in this example. For comparison, AOD values during the 2018 northern California wildfires were <1.0. (b) Detrended time series correlation of DAOD in the Bodélé Depression and DAOD at selected population centres dissipate over space and time. Map shows the location of selected population centres across West Africa as well as Nairobi, Kenya. Right panel shows the time-series correlation of detrended DAOD values between the mapped locations and the Bodélé Depression. In general, as distance to the Bodélé increases, peak correlations in the detrended DAOD time series occur later and are lower in magnitude. Variation in DAOD in Nairobi, a populated location distant and not downwind from the Bodélé, is uncorrelated with dust from the Bodélé.

Extended Data Fig. 3 Estimated shares of particulate matter from natural sources in the exposure data correlate well and exhibit similar spatial patterns with estimates of total aerosols from dust derived from Aeronet ground stations.

For all Aeronet ground stations in our study countries with at least 3 years of data we estimated the share of total aerosols from dust (see Supplement) and compared them to the modelled share of total particulate matter from natural sources (dust and sea salt) for the same locations in the exposure data used in our analysis.10 (a) Comparison of site by year shares. Despite the imperfect comparison between share of aerosols from dust to share of particulate matter from dust and sea salt, there is strong correlation between the data sets. The R2 associated with regressing site by year share of particulate matter from natural sources on site by year share of aerosols from dust is 0.71. The largest discrepancies occur in coastal areas where the share of particulate matter from natural sources is dominated by sea salt. In those cases the share of particulate matter from natural sources can be close to 1 while the share of aerosols from dust can be close to 0. (b) Analogous to panel (a) but for long-run site averages instead of site-year observations. (c) Site average share of particulate matter from natural sources at locations of Aeronet sites in our study countries. (d) Site average share of aerosols from dust. For both data sets, shares are highest in West Africa where they are close to 1 for many locations.

Extended Data Fig. 4 Variation in dust concentration and rainfall over the Bodélé Depression are strong predictors of variation in PM2.5 concentrations in other parts of Africa.

Figure shows estimation results for six different specifications of the first stage (Eq. (1)) relationship between the conditions in the Bodélé instrument and local PM2.5. Models 1 and 4 include DHS cluster, birth year, and country by month fixed effects with household and local weather controls and the dust (Model 1) or rainfall (Model 4) instrument. Models 2 and 5 add additional controls for ENSO conditions and Models 3 and 6 replace DHS cluster fixed effects with a mother fixed effect to restrict comparisons to siblings. The different specifications consistently find dust in the Bodélé Depression is positively associated and rainfall negatively associated with PM2.5 concentrations in both our West African sample (white dots) and full African sample (black dots; see Extended Data Fig. 1 for study locations). Contemporaneous effects are large, statistically significant (F-stats of 17-366), and similar to the main specification without lags (Fig. 2) while lagged effects are small and not statistically significant. A complete description of the fixed effects (dummy variables) and controls is included in the Methods section.

Extended Data Fig. 5 Instrumental variable estimates of the effect of PM2.5 on infant mortality are largely consistent across specifications.

Effects on infant mortality are similar when we isolate variation in local PM2.5 related to dust (Models 1-4) or rainfall (Models 5-8) over the Bodélé Depression. Models 1,5, and 9 include DHS cluster, child birth year, and country by month fixed effects with household and local weather controls (see Methods for details). Models 2 and 6 add controls for ENSO conditions. Models 3,7, and 10 replace DHS cluster fixed effects with mother fixed effects to limit the comparison to siblings. Models 4,8, and 11 drop the regression weights that account for survey sampling scheme and country population. Instrumental Variable (IV) estimates (Model 1-8) that isolate variation in local PM2.5 exposure related to changes in Bodélé conditions are larger than the Ordinary Least Square (OLS) estimates that rely on all sources of variation in local PM2.5 exposure (Models 9-11).

Extended Data Table 1

Overview of parameters used in cost calculations.

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Heft-Neal, S., Burney, J., Bendavid, E. et al. Dust pollution from the Sahara and African infant mortality. Nat Sustain 3, 863–871 (2020). https://doi.org/10.1038/s41893-020-0562-1

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