Researchers at Duke University have developed a method that uses machine learning, satellite
imagery and weather data to autonomously find hotspots of heavy air pollution, city block
by city block.
The technique could be a boon for finding and mitigating sources of hazardous aerosols,
studying the effects of air pollution on human health, and making better informed, socially
just public policy decisions.
Before now, researchers trying to measure the distribution of air pollutants throughout
a city would either try to use the limited number of existing monitors or drive sensors
around a city in vehicles.
But setting up sensor networks is time-consuming and costly, and the only thing that driving
a sensor around really tells you is that roads are big sources of pollutants.
Being able to find local hotspots of air pollution using satellite images is hugely advantageous.
The specific air pollutants that the researchers are interested in are tiny airborne particles
called PM2.5.
These are particles that have a diameter of less than 2.5 micrometers — about three
percent of the diameter of a human hair — and have been shown to have a dramatic effect
on human health because of their ability to travel deep into the lungs.
The Global Burden of Disease study ranked PM2.5 fifth on its list of mortality risk
factors in 2015.
The study indicated that PM2.5 was responsible in one year for about 4.2 million deaths and
103.1 million years of life lost or lived with disability.
A recent study from the Harvard University also found that areas with higher PM2.5 levels
are associated with higher death rates due to COVID-19.
But the Harvard researchers could only access PM2.5 data on a county-by-county level within
the United States.
While a valuable starting point, county-level pollution statistics can’t drill down to
a neighborhood next to a coal-fired power plant versus one next to a park that is 30
miles upwind.
And most countries outside of the Western world don’t have that level of air quality
monitoring.
Ground stations are expensive to build and maintain, so even large cities aren’t likely
to have more than a handful of them.
So while they might give a general idea of the amount of PM2.5 in the air, they don’t
come anywhere near giving a true distribution for the people living in different areas throughout
that city.
In previous work, the researchers showed that satellite imagery, weather data and machine
learning could provide PM2.5 measurements on a small scale.
Building off that work and focusing on Delhi, the team has now improved their methods and
taught the algorithm to automatically find hotspots and cool spots of air pollution with
a resolution of 300 meters — about the length of a New York City block.
The advancement was made by using a technique called residual learning.
The algorithm first estimates the levels of PM2.5 using weather data alone.
It then measures the difference between these estimates and the actual levels of PM2.5 and
teaches itself to use satellite images to make its predictions better.
The researchers then used an algorithm initially designed to adjust uneven illumination in
an image to find areas of high and low levels of air pollution.
Called local contrast normalization, the technique essentially looks for city-block-sized pixels
that have higher or lower levels of PM2.5 than others in their vicinity.
While the exact methods the algorithm teaches itself can’t transfer from city to city,
the algorithm could easily teach itself new methods in different locations.
And while cities might evolve over time in both weather and pollution patterns, the algorithm
shouldn’t have any trouble evolving with them.
Plus, the researchers point out, the number of air quality sensors is only going to increase
in coming years, so they believe their approach will only get better with time.
The results appeared in the journal Remote Sensing.