Dust sweeping across the Southeast U.S. in recent days warns of a growing risk to infants and children in many parts of the world. A Stanford-led study focuses on this dust, which travels thousands of miles from the Sahara Desert, to paint a clearer picture than ever before of air pollution’s impact on infant mortality in sub-Saharan Africa. The paper, published on June 29 in Nature Sustainability, reveals how a changing climate might intensify or mitigate the problem, and points to seemingly exotic solutions to reducing dust pollution that could be more effective and affordable than current health interventions in improving child health.

“Africa and other developing regions have made remarkable strides overall in improving child health in recent decades, but key negative outcomes such as infant mortality remain stubbornly high in some places,” said study senior author Marshall Burke, an associate professor of Earth system science in Stanford’s School of Earth, Energy & Environmental Sciences. “We wanted to understand why that was, and whether there was a connection to air pollution, a known cause of poor health.”

Understanding airborne danger

Children under 5 are particularly vulnerable to the tiny particles, or particulate, in air pollution that can have a range of negative health impacts, including lower birth weight and impaired growth in the first year of life. In developing regions, exposure to high levels of air pollution during childhood is estimated to reduce overall life expectancy by 4-5 years on average.

Quantifying the health impacts of air pollution – a crucial step for understanding global health burdens and evaluating policy choices – has been a challenge in the past. Researchers have struggled to adequately separate out the health effects of air pollution from the health effects of activities that generate the pollution. For example, a booming economy can produce air pollution but also spur developments, such as lower unemployment, that lead to better healthcare access and improved health outcomes.

To isolate the effects of air pollution exposure, the Stanford-led study focuses on dust carried thousands of miles from the Bodélé Depression in Chad – the largest source of dust emissions in the world. This dust is a frequent presence in West Africa and, to a lesser extent, across other African regions. The researchers analyzed 15 years of household surveys from 30 countries across Sub-Saharan Africa covering nearly 1 million births. Combining birth data with satellite-detected changes in particulate levels driven by the Bodélé dust provided an increasingly clear picture of poor air quality’s health impacts on children.

Sobering findings and surprising solutions

The researchers found that a roughly 25 percent increase in local annual mean particulate concentrations in West Africa causes an 18 percent increase in infant mortality. The results expand on a 2018 paper by the same researchers that found exposure to high particulate matter concentrations in sub-Saharan Africa accounted for about 400,000 infant deaths in 2015 alone.

The new study, combined with previous findings from other regions, makes clear that air pollution, even from natural sources, is a “critical determining factor for child health around the world,” the researchers write. Emissions from natural sources could change dramatically in a changing climate, but it’s unclear how. For example, the concentration of dust particulate matter across Sub-Saharan Africa is highly dependent on the amount of rainfall in the Bodélé Depression. Because future changes in rainfall over the Bodélé region due to climate change are highly uncertain, the researchers calculated a range of possibilities for sub-Saharan Africa that could result in anywhere from a 13-percent decline in infant mortality to a 12-percent increase just due to changes in rainfall over the desert. These impacts would be larger than any other published projections for climate change impact on health across Africa.

Safeguarding children against air pollution is nearly impossible in many developing regions because many homes have open windows or permeable roofs and walls, and infants and young children are unlikely to wear masks. Instead, the researchers suggest exploring the possibility of dampening sand with groundwater in the Bodélé region to stop it from going airborne – an approach that has been successful at a small scale in California.

The researchers estimate that deploying solar-powered irrigation systems in the desert area could avert 37,000 infant deaths per year in West Africa at a cost of $24 per life, making it competitive with many leading health interventions currently in use, including a range of vaccines and water and sanitation projects.

“Standard policy instruments can’t be counted on to reduce all forms of air pollution,” said study lead author Sam Heft-Neal, a research scholar at Stanford’s Center on Food Security and the Environment. “While our calculation doesn’t consider logistical constraints to project deployment, it highlights the possibility of a solution that targets natural pollution sources and yields enormous benefits at a modest cost.”

Additional co-authors include Eran Bendavid, an associate professor of medicine at Stanford, member of the Maternal andChild Health Research Institute and an affiliate of the Stanford Woods Institute for the Environment; Jennifer Burney and Kara Voss of the University of California San Diego. Burke is also deputy director of the Center on Food Security and the Environment; and a fellow at the Stanford Woods Institute for the Environment, the Freeman Spogli Institute for International Studies and the Stanford Institute for Economic Policy Research.

The research was supported by the National Science Foundation and the Robert Wood Johnson Foundation.

In combating poverty, like any fight, it’s good to know the locations of your targets.

That’s why Stanford scholars Marshall Burke, David Lobell and Stefano Ermon have spent the past five years leading a team of researchers to home in on an efficient way to find and track impoverished zones across Africa.

The powerful tool they’ve developed combines free, publicly accessible satellite imagery with artificial intelligence to estimate the level of poverty across African villages and changes in their development over time. By analyzing past and current data, the measurement tool could provide helpful information to organizations, government agencies and businesses that deliver services and necessities to the poor.

Details of their undertaking were unveiled in the May 22 issue of Nature Communications.

“Our big motivation is to better develop tools and technologies that allow us to make progress on really important economic issues. And progress is constrained by a lack of ability to measure outcomes,” said Burke, a faculty fellow at the Stanford Institute for Economic Policy Research (SIEPR) and an assistant professor of earth system science in the School of Earth, Energy & Environmental Sciences (Stanford Earth). “Here’s a tool that we think can help.”

Lobell, a senior fellow at SIEPR and a professor of Earth system science at Stanford Earth, says looking back is critical to identifying trends and factors to help people escape from poverty.

“Amazingly, there hasn’t really been any good way to understand how poverty is changing at a local level in Africa,” said Lobell, who is also the director of the Center on Food Security and the Environment and the William Wrigley Fellow at the Stanford Woods Institute for the Environment. “Censuses aren’t frequent enough, and door-to-door surveys rarely return to the same people. If satellites can help us reconstruct a history of poverty, it could open up a lot of room to better understand and alleviate poverty on the continent.”

The measurement tool uses satellite imagery both from the nighttime and daytime. At night, lights are an indicator of development, and during the day, images of human infrastructure such as roads, agriculture, roofing materials, housing structures and waterways, provide characteristics correlated with development.

Then the tool applies the technology of deep learning – computing algorithms that constantly train themselves to detect patterns – to create a model that analyzes the imagery data and forms an index for asset wealth, an economic component commonly used by surveyors to measure household wealth in developing nations.

The researchers tested the measuring tool’s accuracy for about 20,000 African villages that had existing asset wealth data from surveys, dating back to 2009. They found that it performed well in gauging the poverty levels of villages over different periods of time, according to their study.

Here, Burke – who is also a center fellow at the Stanford Woods Institute for the Environment and the Freeman Spogli Institute for International Studies – discusses the making of the tool and its potential to help improve the well-being of the world’s poor.

Why are you excited about this new technological resource?

For the first time, this tool demonstrates that we can measure economic progress and understand poverty interventions at both a local level and a broad scale. It works across Africa, across a lot of different years. It works pretty darn well, and it works in a lot of very different types of countries.

Can you give examples of how this new tool would be used?

If we want to understand the effectiveness of an anti-poverty program, or if an NGO wants to target a specific product to specific types of individuals, or if a business wants to understand where a market’s growing – all of those require data on economic outcomes. In many parts of the world, we just don’t have those data. Now we’re using data from across sub-Saharan Africa and training these models to take in all the data to measure for specific outcomes.

How does this new study build upon your previous work?

Our initial poverty-mapping work, published in 2016, was on five countries using one year of data. It relied on costly, high-resolution imagery at a much smaller, pilot scale. Now this work covers about two dozen countries – about half of the countries in Africa – using many more years of high-dimensional data. This provided underlying training datasets to develop the measurement models and allowed us to validate whether the models are making good poverty estimates.

We’re confident we can apply this technology and this approach to get reliable estimates for all the countries in Africa.

A key difference compared to the earlier work is now we’re using completely publicly available satellite imagery that goes back in time – and it’s free, which I think democratizes this technology. And we’re doing it at a comprehensive, massive spatial scale.

How do you use satellite imagery to get poverty estimates?

We’re building on rapid developments in the field of computer science – of deep learning – that have happened in the last five years and that have really transformed how we extract information from images. We’re not telling the machine what to look for in images; instead, we’re just telling it, “Here’s a rich place. Here is a poor place. Figure it out.”

The computer is clearly picking out urban areas, agricultural areas, roads, waterways – features in the landscape that you might think would have some predictive power in being able to separate rich areas from poor areas. The computer says, ‘I found this pattern’ and we can then assign semantic meaning to it.

These broader characteristics, examined at the village level, turn out to be highly related to the average wealth of the households in that region.

What’s next?

Now that we have these data, we want to use them to try to learn something about economic development. This tool enables us to address questions we were unable to ask a year ago because now we have local-level measurements of key economic outcomes at broad, spatial scale and over time.

We can evaluate why some places are doing better than other places. We can ask: What do patterns of growth in livelihoods look like? Is most of the variation between countries or within countries? If there’s variation within a country, that already tells us something important about the determinants of growth. It’s probably something going on locally.

I’m an economist, so those are the sorts of questions that get me excited. The technological development is not an end in itself. It’s an enabler for the social science that we want to do.

Media Contacts

Adam Gorlick, Stanford Institute for Economic Policy Research: (650) 724-0614, agorlick@stanford.edu