Extreme rainfall increases E. coli exposure for minority communities in Texas

Nobody wants to share a day on the water with E. coli.

The bacteria is a sure sign of fecal contamination, which is washed into waterways from farm fields or sewage systems by rain. The microbes are also dangerous—exposure to E. coli can lead to illness, hospitalization and even death.

Still, as many beachgoers know, it’s not uncommon for E. coli to temporarily shut down lakes and other recreational waters across the U.S. Now, research led by the University of Michigan shows that communities of color in Texas face pronounced risks of E. coli exposure in nearby waters following storms that dump abnormally high amounts of rain.

E. coli is the leading cause of water quality impairments in the United States, and exposure to this pollution is not evenly distributed. We’re also seeing that extreme precipitation has a disproportionate effect on E. coli pollution.”

Xiaofeng Liu, lead author, postdoctoral researcher at the U-M School for Environment and Sustainability and a Schmidt AI in Science fellow at the Michigan Institute for Data and AI in Society

Liu and colleagues worked with E.coli, climate, environmental and socioeconomic data available for the state of Texas between 2001 and 2021. Using computational models, the team could spot when and where the extreme rainfall had the greatest impact on E. coli levels, while uncovering associations between these impacts and socioeconomic factors.

The patterns were complex, but a couple of clear results emerged.

Communities in the north and east with higher percentages of Black residents had higher concentrations of E. coli flushed into their recreational waters by extreme rain in winter.

Predominantly Latino communities—where the majority of residents were of Latin American descent—in the southern and western parts of the state experienced outsize E. coli increases following intense storms in September.

“This is a complicated issue. The communities live in these places all the time, but the rainfall impact is different in different seasons,” Liu said.

There are numerous social, historical, geological and meteorological variables at play for understanding this seasonality and location dependence, which are not all captured by available data, she says.

Still, when coupled with the team’s computational analysis, there is enough data to identify when and where contamination is most likely to occur. And with that knowledge, people can start looking for opportunities to prevent or combat the influx of E. coli.

“This can inform local governments and environmental agencies and help develop targeted policies and targeted water management practices to help these impacted communities,” Liu said.

This work, published in the journal of Science of the Total Environment, was part of a larger project looking at water quality issues and their relationship to social factors.

That project also yielded another recent report—led by Runzi Wang, assistant professor at the University of California, Davis—looking at E. coli levels and trends throughout Texas. That report showed that not only did Black and Latino communities tend to reside near water with higher concentrations of E. coli, so did economically disadvantaged communities.

Liu and her colleagues did not observe a similar correlation between income level and extreme rainfall impacts on E. coli concentrations during their investigation. But they did find that lower-income areas were more likely to experience an increase in extreme rainfall intensity heading forward.

“The regions with this increasing trend also tended to have higher percentages of lower-income residents,” Liu said. “So even though there isn’t a correlation now, climate variability could amplify the effect for lower-income communities in the future.”

The team focused their initial study on Texas because E. coli is a serious and known problem. About a third of the streams in Texas are polluted by bacteria, Liu said, but the state also has a robust monitoring system.

Because of that, the team could validate their methods while also performing useful analysis. The researchers now plan to extend their work to other locations in the U.S.

“With this study, we wanted to demonstrate our framework to connect surface water quality with social factors,” Liu said. “Our model is definitely applicable to other regions.”