How Artificial Intelligence Can Help Save Us from Air Pollution
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esearchers find AI may outperform traditional models, which could give more advance warning of bad air days, and reduce harmful exposures and hospital visits. As air quality plummets across the U.S. this summer, researchers have a glimmer of good news. Artificial intelligence may soon provide advanced warning of future pollution events, which could help hospitals prepare for the uptick in pollution-related illnesses, or even reduce people’s exposure entirely. A spike in air pollution often leads to a spike in hospital admissions, as it can exacerbate asthma and other preexisting respiratory conditions, cause upper respiratory tract infections, or increase the likelihood of stroke. But it’s currently impossible to prepare for these spikes due to the constraints of existing air quality forecasts, which are only accurate up to three days in advance, Yunsoo Choi, associate professor of atmospheric chemistry from the University of Houston, told EHN. In that short amount of time, one of the only things we can do to protect ourselves is to limit time spent outdoors. Related: Measuring Houston’s environmental injustice from space But now, through the use of artificial intelligence (AI) technology, Choi and 40
Automate Sept-Nov 2021
the University of Houston’s Air Quality Forecasting and Modeling Lab created a new model that can predict ozone pollution up to 14 days ahead of time. While ozone in the upper atmosphere shields us from the sun’s ultraviolet radiation, ozone at ground-level is a harmful pollutant that irritates our lungs. Since it is formed in the atmosphere on hot, sunny days, we will see unprecedented spikes in ozone due to climate change, similar to what we witnessed across the U.S. during the country’s most recent heat wave.
Artificial intelligence to improve air quality forecasting Traditional air quality forecasts are created by numerical models, which are essentially sophisticated calculators. They solve many lines of mathematical equations to determine how much pollution will be produced, and how it will be transported across an area at a given point in time.
These models could give local governments more opportunities to control pollution emission sources. “Having a model that runs faster allows [local governments] to explore a greater variety of scenarios of how they can improve [air quality],” Sherri Hunt, the Principal Associate National Program Director for the Air, Climate and Energy Research Program at the Environmental Protection Agency (EPA), told EHN. Hunt was not involved in this study. For example, if researchers determine the future high ozone event will be caused by cars, then policymakers can suggest ways to minimize the number of cars on the road. In addition, with Choi’s AI model, “we could decide how we’re going to staff the emergency room” during bad air events, Hunt said.
Yunsoo Choi, left, associate professor in the Department of Earth and Atmospheric Sciences at the University of Houston, and Ph.D. student Alqamah Sayeed developed a new model to better predict ozone levels. (University of Houston)
These equations aren’t solved only once. They have to be solved for each hour the model forecasts into the future, which takes a lot of time and computational power. “In order to forecast two or three days... that takes a few hours” even with a supercomputer, said Choi.
