Power plants are prolific particulate producers. To generate energy, power plants burn fossil fuels, and the combustion spews gases and fine specks of pollution into the air—particulate matter less than 2.5 micrometers in size, what researchers call PM2.5.
Air pollution affects everyone, but experts are now warning that exposure levels can weigh heavier on certain racial and ethnic communities, independent of their income levels. And even if your state has stringent pollution controls, researchers note that the effects of particulates can reach far beyond the shadows of power plants, crossing state lines.
Pollution Beyond Borders
In 2014, more than 16,000 premature deaths attributed to exposure to PM2.5 from power plants occurred in the United States, but the exact distribution of pollution was unclear.
Researchers have now delved into data to understand how this pollution exposure differs by demographic groupings. Specifically, the team looked at health-related impacts broken down by race, income, and geography.
“We were interested in [learning] who inhales the air pollution from power plants,” said Julian Marshall, an air pollution researcher at the University of Washington. He and his coauthors published their work in Environmental Science and Technology.
The researchers’ first step was to see what types of PM2.5 were being emitted from power plants burning fossil fuels (coal, natural gas, petroleum). They tracked the distribution of the pollution, its chemical reactions, the physics of the particulates, and the meteorology in the area around the power plants.
Complex modeling of air pollution normally takes a lot of time and effort, especially for huge areas like the continental United States. Considering this, the team took a different route to expedite their results while maintaining the scientific integrity of the modeling.
“The results we have here are a [product] of a new approach to modeling, which is called [a] reduced complexity model, or RCM,” explained Marshall.
The streamlined approach allowed the team to run the RCM for almost every power plant in the United States. The model also allowed the team to consider how pollution in one state is affecting other states. For example, Ohio gets 37% of its power plant emissions drifting in from Indiana.
Pennsylvania had the highest number of air pollution–related fatalities in 2014, with almost 2,000 premature deaths. Pollution sources came from inside the state, as well as from West Virginia, Ohio, Indiana, Illinois, and Kentucky.
After seeing where pollution was going, the team overlaid the data with census information to see what communities might be affected the most by power plant pollution. Marshall said that demographics like race, ethnicity, and household income were all considered.
They found that average exposure to air pollution was highest for black Americans, followed by non-Latino white populations. For every 100,000 people, about seven black Americans died prematurely, while six white Americans died. Other race/ethnic groups averaged four deaths per 100,000 people.
When the researchers looked at mortality based on income, they found that low-income households (those making less than $10,000 per year) had one additional death per 100,000 people compared with high-income households (those making more than $200,000 per year). This indicates that individuals with lower incomes had higher exposures to PM2.5.
Despite the exposure rates varying by income, the trends of pollution exposure among specific races held.
“In our society, where people live and where there is pollution are not random [factors],” said Marshall. He added that his team’s findings are important when considering issues of environmental justice in underserved communities.
“It’s especially relevant these days because of what the federal government is doing—or actually not doing—around air pollution and around these issues in general,” he said.
—Sarah Derouin (@Sarah_Derouin), Freelance Journalist
Derouin, S. (2019), Some communities feel the effects of air pollution more than others, Eos, 100, https://doi.org/10.1029/2019EO136875. Published on 20 November 2019.
Text © 2019. The authors. CC BY-NC-ND 3.0
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