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.
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
Data are available at https://github.com/burke-lab/NatSus2020.
Code to replicate all results is available at https://github.com/burke-lab/NatSus2020.
Shindell, D., Faluvegi, G., Seltzer, K. & Shindell, C. Quantified, localized health benefits of accelerated carbon dioxide emissions reductions. Nat. Clim. Change 8, 291–295 (2018).
Burnett, R. et al. Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter. Proc. Natl Acad. Sci. USA 115, 9592–9597 (2018).
Lelieveld, J., Haines, A. & Pozzer, A. Age-dependent health risk from ambient air pollution: a modelling and data analysis of childhood mortality in middle-income and low-income countries. Lancet Planet. Health 2, e292–e300 (2018).
Cesur, R., Tekin, E. & Ulker, A. Air pollution and infant mortality: evidence from the expansion of natural gas infrastructure. Econ. J. 127, 330–362 (2017).
Arceo, E., Hanna, R. & Oliva, P. Does the effect of pollution on infant mortality differ between developing and developed countries? Evidence from Mexico City. Econ. J. 126, 257–280 (2016).
Chay, K. Y. & Greenstone, M. Air Quality, Infant Mortality, and the Clean Air Act of 1970 Working Paper No. 10053 (National Bureau of Economic Research, 2003).
Knittel, C. R., Miller, D. L. & Sanders, N. J. Caution, drivers! children present: traffic, pollution, and infant health. Rev. Econ. Stat. 98, 350–366 (2016).
Chay, K. Y. & Greenstone, M. The impact of air pollution on infant mortality: evidence from geographic variation in pollution shocks induced by a recession. Q. J. Econ. 118, 1121–1167 (2003).
He, G., Fan, M. & Zhou, M. The effect of air pollution on mortality in China: evidence from the 2008 Beijing Olympic Games. J. Environ. Econ. Manag. 79, 18–39 (2016).
Van Donkelaar, A. et al. Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors. Environ. Sci. Technol. 50, 3762–3772 (2016).
Aliaga, A. & Ren, R. Optimal Sample Sizes for Two-stage Cluster Sampling in Demographic and Health Surveys Working Paper No. 30 (DHS, 2006).
Miller, G. & Urdinola, B. P. Cyclicality, mortality, and the value of time: the case of coffee price fluctuations and child survival in Colombia. J. Polit. Econ. 118, 113–155 (2010).
Heft-Neal, S., Burney, J., Bendavid, E. & Burke, M. Robust relationship between air quality and infant mortality in Africa. Nature 559, 254–258 (2018).
Adhvaryu, A. et al. Dust and death: evidence from the West African Harmattan. Econ. J. (in the press).
Foreman, T. The Effect of Dust Storms on Child Mortality in West Africa Working Paper No. 47 (CDEP-CGEG, 2018).
Evan, A. T., Flamant, C., Gaetani, M. & Guichard, F. The past, present and future of African dust. Nature 531, 493–495 (2016).
Koren, I. et al. The Bodélé Depression: a single spot in the Sahara that provides most of the mineral dust to the Amazon forest. Environ. Res. Lett. 1, 014005 (2006).
Washington, R. et al. Dust as a tipping element: the Bodélé Depression, Chad. Proc. Natl Acad. Sci. USA 106, 20564–20571 (2009).
Wagner, R., Schepanski, K., Heinold, B. & Tegen, I. Interannual variability in the Saharan dust source activation—toward understanding the differences between 2007 and 2008. J. Geophys. Res. Atmos. 121, 4538–4562 (2016).
Voss, K. K. & Evan, A. T. A new satellite-based global climatology of dust aerosol optical depth. J. Appl. Metereol. Climatol. 59, 83–102 (2020).
Washington, R. & Todd, M. C. Atmospheric controls on mineral dust emission from the Bodélé Depression, Chad: the role of the low level jet. Geophys. Res. Lett. 32, L17701 (2005).
Van Donkelaar, A., Martin, R. V., Brauer, M. & Boys, B. L. Use of satellite observations for long-term exposure assessment of global concentrations of fine particulate matter. Environ. Health Perspect. 123, 135–143 (2015).
Brooks, N. & Legrand, M. in Linking Climate Change to Land Surface Change (eds McLaren, S. J. & Kniveton, D. R.) 1–25 (Springer, 2000).
Wang, W., Evan, A. T., Lavaysse, C. & Flamant, C. The role the Saharan heat low plays in dust emission and transport during summertime in North Africa. Aeolian Res. 28, 1–12 (2017).
Angrist, J. & Imbens, G. Identification and estimation of local average treatment effects. Econometrica 62, 467–475 (1994).
Angrist, J. D. & Krueger, A. B. Instrumental variables and the search for identification: from supply and demand to natural experiments. J. Econ. Perspect. 15, 69–85 (2001).
Wang, C., Dong, S., Evan, A. T., Foltz, G. R. & Lee, S.-K. Multidecadal covariability of North Atlantic sea surface temperature, African dust, Sahel rainfall, and Atlantic hurricanes. J. Clim. 25, 5404–5415 (2012).
Biasutti, M. Forced Sahel rainfall trends in the CMIP5 archive. J. Geophys. Res. Atmos. 118, 1613–1623 (2013).
Skinner, C. B. & Diffenbaugh, N. S. Projected changes in African easterly wave intensity and track in response to greenhouse forcing. Proc. Natl Acad. Sci. USA 111, 6882–6887 (2014).
Rodríguez-Fonseca, B. et al. Variability and predictability of West African droughts: A review on the role of sea surface temperature anomalies. J. Clim. 28, 4034–4060 (2015).
Yoshioka, M. et al. Impact of desert dust radiative forcing on Sahel precipitation: relative importance of dust compared to sea surface temperature variations, vegetation changes, and greenhouse gas warming. J. Clim. 20, 1445–1467 (2007).
Yu, K., D’Odorico, P., Bhattachan, A., Okin, G. S. & Evan, A. T. Dust–rainfall feedback in West African Sahel. Geophys. Res. Lett. 42, 7563–7571 (2015).
Wang, W., Evan, A. T., Flamant, C. & Lavaysse, C. On the decadal scale correlation between African dust and Sahel rainfall: the role of Saharan heat low-forced winds. Sci. Adv. 1, e1500646 (2015).
MacDonald, A. M., Bonsor, H. C., Dochartaigh, B. É. Ó. & Taylor, R. G. Quantitative maps of groundwater resources in Africa. Environ. Res. Lett. 7, 024009 (2012).
Burney, J., Woltering, L., Burke, M., Naylor, R. & Pasternak, D. Solar-powered drip irrigation enhances food security in the Sudano-Sahel. Proc. Natl Acad. Sci. USA 107, 1848–1853 (2010).
2008 Owens Valley PM10 Planning Area Demonstration of Attainment State Implementation Plan (Great Basin Unified Air Pollution Control District, 2008); https://go.nature.com/37qhRIP
Macroeconomics and Health: Investing in Health for Economic Development (Commission on Macroeconomics and Health, 2001).
Jamison, D. T. et al. Disease Control Priorities Vol. 9 (The World Bank, 2017).
Li, Y. et al. Climate model shows large-scale wind and solar farms in the Sahara increase rain and vegetation. Science 361, 1019–1022 (2018).
Bristow, C. S., Hudson-Edwards, K. A. & Chappell, A. Fertilizing the Amazon and equatorial Atlantic with West African dust. Geophys. Res. Lett. 37, L14807 (2010).
Hsu, N. et al. Enhanced Deep Blue aerosol retrieval algorithm: the second generation. J. Geophys. Res. Atmos. 118, 9296–9315 (2013).
Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).
Bartik, T. J. Who Benefits from State and Local Economic Development Policies? (W.E. Upjohn Institute for Employment Research, 1991).
Owens Valley PM10 Planning Area (OVPA) State Implementation Plan (SIP) Prior to the 2016 SIP (Great Basin Unified Air Pollution Control District, 2014); https://gbuapcd.org/District/AirQualityPlans/SIP_Archive/
Owens Valley PM10 State Implementation Plan (Great Basin Unified Air Pollution Control District, 2016); https://gbuapcd.org/District/AirQualityPlans/OwensValley/
Gillette, D., Ono, D. & Richmond, K. A combined modeling and measurement technique for estimating windblown dust emissions at Owens (dry) Lake, California. J. Geophys. Res. Earth Surface 109, F01003 (2004).
Groeneveld, D. P., Watson, R. P., Barz, D. D., Silverman, J. B. & Baugh, W. M. Assessment of two methods to monitor wetness to control dust emissions, Owens Dry Lake, California. Int. J. Remote Sensing 31, 3019–3035 (2010).
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.
The authors declare no competing interests.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
(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).
Overview of parameters used in cost calculations.
About this article
Cite this article
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
Nature Human Behaviour (2021)
Nature Communications (2021)