No one wants to share a day on the water with E. coli.
The bacteria is a sure sign of fecal contamination, which rain washes into waterways from agricultural fields or sewage systems. Germs are dangerous, too: 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 close lakes and other recreational waters across the United States. Now, research led by the University of Michigan shows that communities of color in Texas face pronounced risks of exposure to E. coli. in nearby waters after storms that dump abnormally high amounts of rain.
“E. coli is the leading cause of water quality deterioration in the United States, and exposure to this pollution is not evenly distributed. We are also seeing that extreme precipitation has a disproportionate effect on E. coli pollution.” said lead author Xiaofeng Liu, a postdoctoral researcher in the U-M School of Environment and Sustainability and a Schmidt AI in Science Fellow at the Michigan Institute for Data and AI in Society.
Liu and his colleagues worked with climate, environmental, and socioeconomic E. coli data available for the state of Texas between 2001 and 2021. Using computer models, the team was able to detect when and where extreme precipitation had the greatest impact on E levels. .coli. while discovering associations between these impacts and socioeconomic factors.
The patterns were complex, but a couple of clear results emerged.
Northern and eastern communities with higher percentages of Black residents had higher concentrations of E. coli released into their recreational waters due to extreme winter rainfall.
Predominantly Latino communities, where most residents were of Latin American descent, in the southern and western parts of the state experienced huge increases in E. coli following intense storms in September.
“This is a complicated issue. Communities live in these places all the time, but the impact of rainfall is different in different seasons,” Liu said.
There are numerous social, historical, geological and meteorological variables at play in understanding this seasonality and location dependence, not all of which are reflected in the available data, he says.
Still, when combined 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 begin to look 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 affected communities,” Liu said.
This work, published in the magazine of Total Environmental Science, It was part of a larger project that analyzed water quality problems and their relationship to social factors.
That project also produced another recent report, led by Runzi Wang, an assistant professor at the University of California, Davis, that analyzes E. coli levels and trends across Texas. That report showed that not only black and Latino communities tended to reside near water with higher concentrations of E. coli, but also economically disadvantaged communities.
Liu and his colleagues did not observe a similar correlation between income level and the impacts of extreme precipitation on E. coli concentrations during their research. But they did find that low-income areas were more likely to experience an increase in the intensity of extreme precipitation in the future.
“Regions with this increasing trend also tended to have higher percentages of low-income residents,” Liu said. “So, although there is no correlation now, climate variability could amplify the effect in low-income communities in the future.”
The team focused its initial study on Texas because E. coli is a serious and known problem. About a third of Texas streams are contaminated by bacteria, Liu said, but the state also has a robust monitoring system.
Thanks to this, the team was able to validate their methods and at the same time perform useful analyses. The researchers now plan to extend their work to other locations in the US.
“With this study, we wanted to demonstrate our framework for connecting surface water quality with social factors,” Liu said. “Our model is definitely applicable to other regions.”