(Press-News.org)
U.S. Environmental Protection Agency (EPA) air quality monitors are disproportionally located in predominately white neighborhoods, according to University of Utah research. The EPA’s network consistently failed to capture air quality in communities of color across six major pollutants, particularly lead and sulfur dioxide, followed by ozone and carbon monoxide.
EPA regulatory monitors are the key data source driving decisions about pollution reduction, urban planning and public health initiatives. Without equal monitor distribution, the data may misrepresent pollution concentrations, leaving marginalized groups at risk.
“It’s the question behind the question. Researchers, policymakers, we all use air quality data, but whose air is it measuring?” said Brenna Kelly, doctoral student at the U and lead author of the study. “Even though this data is of really high quality, that doesn’t mean that it’s high quality for everyone.”
Research has shown that marginalized communities have the highest rates of exposure to air pollution, but the studies assumed that the data represented all neighborhoods equally. This study is the first to assess disparities in the monitors themselves for all U.S. Census groups, on a neighborhood scale. While disparities existed for all non-white groups, the largest were for Native Hawaiians and other Pacific Islanders, followed by American Indian and Alaska Native populations.
Air quality research and analysis often require artificial intelligence (AI) tools to process the massive volumes of data. While bias in AI algorithms is well-known, the study exemplifies another ethical issue for big-data users—the chance that the datasets themselves are inherently biased.
“If there was a disparity for just one type of monitor, it could conceivably be accidental or just poor design,” said coauthor Simon Brewer, associate professor of geography and executive committee member of the U’s ONE-U Responsible AI Initiative. “The fact that it's a consistent pattern across all pollutants suggests that the decision-making process needs to be looked at carefully—these monitors are not being distributed equitably.”
The study was published in journal JAMA Network Open on Dec. 4, 2024.
Air quality is hyperlocal and can change dramatically from street to street. The authors mapped monitor locations and neighborhood demographics to the census-block level, one of the U.S. Census Bureau’s smallest units for residential patterns. Using the EPA Air Quality System Regulatory Monitoring Repository, they identified monitors for six major air pollutants dangerous to human health—lead, ozone, nitrogen dioxide, sulfur dioxide, carbon monoxide and particulate matter. They used the 2022 American Community Survey Census to estimate the racial and ethnic composition for every census-block in the country. Adjusting for population size, the researchers found systemic monitoring disparities for each criteria pollutant. Relative to the white non-Hispanic population, all groups were associated with fewer lead, ozone, nitrogen dioxide and particulate matter monitors.
Kelly got curious about the EPA’s air quality monitoring network while pursuing her doctoral research in population health sciences, focusing on the risks of air pollution exposure to pregnant people. Epidemiology research identifies the factors that contribute to diseases within a population. Until now, there’s been an underlying assumption that the data represents air quality problems everywhere equally, she explained.
“It's not just that we're missing one pollutant type for one group, it's that we understand less about everything for all these groups. That's concerning,” Kelly said. “If I want to relate air pollution exposure to a disease, I need to measure it well. If I have a better understanding of air quality for one group of people, that’s going to produce biased results.”
Air quality and population health are just two of many fields grappling with the challenges of using big data and AI responsibly. The One-U Responsible AI Initiative is the U’s recent effort to bring together experts to develop best practices.
“This study is particularly relevant in an increasingly data-driven society. One of the goals of the Responsible AI Initiative is to study the fair application of artificial intelligence methods,” Brewer said. “Our results suggest that biases in the data may be as important to consider as any algorithmic bias.”
Other authors include University of Utah researchers Tracy Onega of Population Health Sciences, Thomas Cova of the School of Environment, Society and Sustainability and Michelle Debbink of the Departments of Obstetrics and Gynecology.
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