No one wants to share a day on the water with E. coli.
This bacterium signals fecal contamination, typically entering waterways from agricultural fields or sewage systems during rainfall. E. coli can pose serious health risks, leading to illness, hospitalizations, and even fatalities.
However, many beachgoers are aware that elevated levels of E. coli can result in temporary closures of lakes and recreational areas across the U.S. Recent research from the University of Michigan highlights that communities of color in Texas are particularly vulnerable to E. coli exposure in local waters after severe storms that deliver excessive rainfall.
“E. coli is a primary cause of water quality issues in the United States, and the exposure to this pollution is not evenly distributed. We also observe that extreme precipitation significantly affects E. coli contamination,” explained Xiaofeng Liu, the lead author and a postdoctoral researcher at the U-M School for Environment and Sustainability, as well as a Schmidt AI in Science fellow at the Michigan Institute for Data and AI in Society.
Liu and her team analyzed E. coli, climate, environmental, and socioeconomic data from Texas covering the years 2001 to 2021. They utilized computational models to identify when and where extreme rainfall most significantly impacted E. coli levels, while also exploring links between these effects and socioeconomic variables.
The results were intricate, but a few clear findings emerged.
In northern and eastern Texas, areas with larger Black populations faced higher levels of E. coli contamination in their recreational waters due to extreme winter rainfall.
Meanwhile, predominantly Latino communities—where most residents are of Latin American descent—located in the southern and western regions of the state saw significant spikes in E. coli levels following heavy storms in September.
“This issue is quite complex. The residents of these communities experience varying impacts of rainfall throughout different seasons,” Liu noted.
Numerous social, historical, geological, and meteorological factors influence this seasonality and geographic variability, she added, many of which are not fully captured by the available data.
However, combined with the team’s computational analysis, the existing data is sufficient to pinpoint when and where contamination is most likely to happen. This understanding can pave the way for proactive measures to prevent or reduce E. coli influx.
“This information can guide local governments and environmental agencies in creating focused policies and water management practices specifically for these affected communities,” Liu said.
This research, published in the journal Science of the Total Environment, is part of a broader initiative examining water quality issues in relation to social factors.
This initiative also led to another recent study by Runzi Wang, an assistant professor at the University of California, Davis, which investigated E. coli levels and trends across Texas. It found that Black and Latino communities often live near waters with elevated E. coli levels, as do economically disadvantaged communities.
Although Liu and her team did not find a similar connection between income levels and the effects of extreme rainfall on E. coli concentrations during their research, they did discover that lower-income areas are more likely to be affected by increasing rainfall intensity in the future.
“Regions experiencing this upward trend in rainfall also tend to have larger proportions of lower-income residents,” Liu explained. “So, while there isn’t a current correlation, climate variability could intensify the impact on lower-income communities moving forward.”
The team concentrated on Texas for their initial study due to the serious and well-established issues with E. coli there. Liu noted that approximately one-third of Texas’ streams are polluted by bacteria, but the state has a thorough monitoring system in place.
This allowed the researchers to validate their methods while conducting valuable analyses. They now plan to expand their research to other regions in the U.S.
“Through this study, we aimed to showcase our framework for linking surface water quality with social factors,” Liu stated. “Our model can certainly be applied to other areas.”
