NC State Extension Publications

## What are fecal coliform bacteria?

Fecal coliform bacteria, the microscopic organisms that live in the waste material, or feces, of humans and animals, can be found in waste treatment facilities and the natural environment. Most fecal coliforms do not cause illness, although their presence in water indicates that other disease-causing organisms in feces, or pathogens, may be present (Häder et al. 2020). Testing for all possible fecal pathogens is not possible or practical. Instead, fecal coliform bacteria are measured in water samples and used as a proxy measure of the overall presence of pathogens (EPA 2010; Wang and Deng 2019). The water samples are collected through sampling programs organized by local and state departments such as the North Carolina Division of Marine Fisheries, which collects hundreds of water quality samples on a regular basis. However, because coastal systems are dynamic, these efforts struggle to capture rapid changes in fecal coliform bacteria concentrations and the overall presence of pathogens. Computer models that are capable of predicting fecal coliform contamination in the present (nowcasts) and future (forecasts) can help practitioners, regulators, and the public better understand and manage coastal systems.

## How do fecal coliforms affect coastal systems?

Fecal coliforms are used to assess coastal waters for potential contamination of shellfish and threats to public health. When fecal coliform levels are low in a coastal area, we assume that fecal pathogens are low as well. Water with low coliform concentrations is considered safe for shellfish harvest and aquacultural activities (Almeida and Soares 2012). However, when fecal coliform levels are high and exceed the federal and state designated standards, the water is deemed unsafe and can be closed for shellfish harvest (EPA 2010). Water closures create significant burdens for coastal communities. Recreational areas that are closed may result in financial losses of more than $15,000 per day for nearby communities (EPA 2010), while frequent closures of shellfish harvesting areas have contributed to an$18B seafood deficit domestically (NOAA 2021).

Closure of shellfish harvest areas may be long-term or temporary. Long-term closures are required for waters with persistent water quality issues, while short-term closures are used for waters that experience a brief water quality issue. Temporary closures are usually prompted by spills from wastewater treatment plants or large rain storms, which briefly contaminate the coastal system. Wastewater spills introduce human waste material into rivers and upstream waters, which eventually flow into coastal systems (Verhougstraete et al. 2015). Rain storms wash overland fecal matter from humans (malfunctioning or overflowing septic systems) or animals (pets, wildlife, and animal agriculture operations) into nearshore waters (Figure 1). Although the storms that cause fecal contamination of coastal waters are often large with over 4 inches of rainfall in 24 hours, some nearshore waters are susceptible to contamination after smaller rain storms. For example, some shellfish harvest areas in North Carolina will close temporarily after 1 inch of rainfall within 24 hours. Whether a coastal waterway is vulnerable to contamination after a small storm often depends on the speed with which stormwater runoff moves off the land and into nearshore waters, the size of the coastal waterway, and the strength of the tides that influence the system. A small coastal waterway that does not experience a lot of tidal flushing and receives stormwater runoff shortly after a storm is more likely to be contaminated after a relatively small storm than a large, well-flushed coastal waterbody.

Coastal waters can also be contaminated from other localized events, such as unusually high tides or a flock of migratory birds passing overhead (Figure 2). These smaller scale events are typically harmless, although under the right conditions, can cause high fecal coliform concentrations (Thoe et al. 2014; EPA 2016). It is important to remember that water quality can change quickly in the span of minutes to days (Thoe et al. 2015). Given the right conditions, a small addition of waste material can be sufficient to contaminate a coastal system.

Figure 1: Rain storms wash fecal coliform bacteria and pathogens from an overland area to coastal waters and cause contamination.

Illustration by Sheila Saia.

Figure 2: Small scale events like flocks of birds may cause high coliform concentrations.

Photograph taken by Natalie Nelson.

## How are fecal coliform concentrations measured?

Manual sampling is the most common procedure used for measuring fecal coliform concentrations in coastal waters (Wong et al. 2009). Fecal coliform analysis, which measures the abundance of fecal coliforms in a certain volume of water, is obtained through a multiple step process. First, a water sample is collected manually from the coastal system (Figure 3), preserved on ice, and then transported to an analytical laboratory. At the laboratory, the water sample is analyzed for fecal coliform concentration with a federally and state- approved method (EPA 2010).

Manual sampling is considered a reliable approach for accurately measuring fecal coliform concentrations in coastal waters. The downside of the manual sampling process is the long delay between the time of sampling to the production of the laboratory results. For coastal states in general, the typical manual sampling process requires one to two days from collecting the samples, transporting the samples to the laboratory, and then analyzing them for fecal coliform concentration (Thoe et al. 2014; EPA 2016). During the time lag between sampling and completion of the lab analyses, coastal waters may remain closed even if they are actually safe for recreation, fishing, and aquaculture. A coastal area can also be contaminated through a smaller-scale, localized event that remains undetected. Although water sampling and analysis are designed to accurately assess the risk of humans becoming sick from fecal pathogens, it is not feasible to sample all areas immediately after a potential pollution event.

Figure 3: Water samples are manually collected by dipping a bottle into the water and filling it.

Illustration by Sheila Saia.

## How can we reduce delays associated with manual sampling?

Models help to address the limitations of manual sampling by predicting fecal coliform concentrations in real-time or for future events (Frick et al. 2008; Thoe et al. 2014). Models simulate current water quality conditions (“nowcasts”) or future water quality conditions (“forecasts”) in a coastal ecosystem by using statistical and computer-driven methods. Water quality models use a variety of information, such as past and current water quality, and meteorological and land-use measurements to identify relationships between environmental conditions and fecal coliform concentrations (Badgley, et al. 2019). Water quality models are then tested against past data to ensure accuracy. These water quality models can greatly reduce the time required for estimating fecal coliform concentrations in coastal areas. Although the models can reduce or eliminate time lags associated with closing waters after a contamination event, they only provide estimates of fecal coliform concentrations. To confirm the contamination status of a waterbody, a manual sample must be collected and analyzed with approved laboratory methods that meet the federal and state requirements for reopenings. Information from water quality models helps to manage water closures more efficiently and to determine times when additional manual water samples are needed.

## What are nowcast fecal coliform models?

Nowcast models provide estimates of fecal coliform concentrations in a coastal area at the current time using real-time environmental data such as wind, rain, and tidal conditions. Nowcast models can estimate fecal coliform concentrations for the past 0 to 12 hours (Frick et al. 2008). Within this time range, it is possible for nowcast models to capture smaller-scale contamination events, such as those caused by high tides, with reasonable accuracy and to provide a faster method that can issue a closure for contaminated waters. Examples of nowcasting tools and fecal coliform models currently used for coastal water management include:

• Virtual Beach is a statistical tool developed by the Environmental Protection Agency. Virtual Beach uses wind and other variables to predict pathogen indicator levels for freshwater and saltwater beach sites.
• Chattahoochee River BacteriAlert is a predictive model that uses turbidity and other data to compute E. coli concentrations in the Chattahoochee River National Recreation Area. Chattahoochee River BacteriAlert is developed and maintained by the U.S. Geological Survey.
• How’s the Beach? is a predictive tool that estimates current Enterococci conditions at multiple public beaches on the eastern coast of the United States. How’s the Beach was developed as a joint initiative of the University of South Carolina, Southeast Coastal Ocean Observing Regional Association, and the Integration and Application Network at the University of Maryland.
• Heal the Bay’s Beach Report Card is a statistical model developed as a joint project by Heal the Bay, Stanford University, and the University of California – Los Angeles. The Beach Report Card uses wind, rainfall, and other variables to estimate current fecal bacteria levels in the surf zone of beaches along the western coast of the United States.

## What are fecal coliform forecast models?

Forecast models provide future estimates of fecal coliform concentrations in a coastal area. Like nowcast models, forecast models use environmental data as the basis of their predictions. Forecast models often build on other types of forecast information, such as weather forecasts. Forecast fecal coliform models attempt to predict the fecal coliform concentrations of the next few days (Frick et al. 2008). This type of model is important because it helps others prepare for the future and provides some warning of a closure that is likely to occur. Fewer forecast fecal coliform models exist as compared to nowcast models. An example of a forecast model currently in development is:

• ShellCast is forecast tool that predicts temporary shellfish harvest area closures (see Saia et al. 2021) as a function of rainfall in North Carolina coastal waters. ShellCast is developed and maintained by North Carolina State University.

## Conclusions

Computer models can help simulate current conditions and predict future water quality conditions in coastal areas. These estimates can help growers, regulators, and coastal communities make wise decisions about water sampling, shellfish harvesting, recreation, and other important coastal activities. Several current models are now available. Ongoing development of these models is helping enhance simulations and predictions.

## References

Almeida, Caterina, and Florbela Soares. 2012. “Microbiological Monitoring of Bivalves from The Ria Formosa Lagoon (South Coast of Portugal): A 20 years of Sanitary Survey.” Marine Pollution Bulletin 64, no. 2: 252-262.

Badgley, Brian D., Meredith K. Steele, Catherine Cappellin, Julie Burger, Jinshi Jian, Timothy P. Neher, Megan Orentas, and ReganWagner. 2019. “Fecal Indicator Dynamics at the Watershed Scale: Variable Relationships with Land Use, Season, and Water Chemistry.” Science of The Total Environment 697: 134113.

Environmental Protection Agency. 2010. Sampling and Consideration of Variability (Temporal and Spatial) for Monitoring of Recreational Waters (Report No. EPA-823-R-10-005).

Environmental Protection Agency. 2016. Six Key Steps for Developing and Using Predictive Tools at Your Beach (Report No. EPA-820-R-16-001).

Environmental Protection Agency. 2020. Virtual Beach. Accessed March 17, 2021.

Frick, Walter G., Zhongfu Ge, and Richard G. Zepp. 2008. “Nowcasting and Forecasting Concentrations of Biological Contaminants at Beaches: A Feasibility and Case Study.” Environmental Science & Technology 42, no. 13: 4818-4824.

Häder, Donat, Anastazia Banaszak, Virginia E.Villafañe, Maite Narvarte, Raúl A. González, and E. Walter Helbling. 2020. “Anthropogenic Pollution of Aquatic Ecosystems: Emerging Problems with Global Implications.” Science of the Total Environment 713:136586.

National Oceanic and Atmospheric Administration. 2021. Fisheries of the United States. Accessed March 10, 2021.

North Carolina State University. 2021. ShellCast. Accessed March 17, 2021.

Saia, Sheila M., Natalie Nelson, Sierra Young, and Steven Hall. 2021. Shellfish Leases and Harvest Closures along the North Carolina Coast. AG-898. Raleigh, NC: NC State Extension.

Thoe, W., M. Gold, A. Griesbach, M. Grimmer, M.L.Taggart, and A.B. Boehm. 2014. “Predicting Water Quality at Santa Monica Beach: Evaluation of Five Different Models for Public Notification of Unsafe Swimming Conditions.” Water Research 67: 105-117.

Thoe, W., M. Gold, A. Griesbach, M. Grimmer, M.L.Taggart, and A.B. Boehm. 2015. “Sunny with a Chance of Gastroenteritis: Predicting Swimmer Risk at California Beaches.” Environmental Science & Technology, 49, no. 1: 423-431.

United States Geological Service. 2021. BacteriAlert Realtime Monitoring. Accessed March 17, 2021.

Heal the Bay. 2021. Beach Report Card: Heal The Bay. Accessed March 17, 2021.

University of South Carolina. 2021. How's the Beach? Accessed March 17, 2021.

Verhougstraete, Marc P., Sherry L. Martin, Anthony D. Kendall, David D. Hyndman, and Joan B. Rose. 2015. “Linking Fecal Bacteria in Rivers to Landscape, Geochemical and Hydrologic Factors and Sources at the Basin Scale.” PNAS 112, no. 33: 10419-10424.

Wang, Jiao, and Zhiqiang Deng. 2019. “Modeling and Predicting Fecal Coliform Bacteria Levels in Oyster Harvest Waters along Louisiana Gulf Coast.” Ecological Indicators 101: 212-220.

Wong, Mark, Lekha Kumar, Tracie M. Jenkins, Irene Xagoraraki, Mantha S. Phanikumar, and Joan B.Rose. 2009. “Evaluation of Public Health Risks at Recreational Beaches in Lake Michigan Via Detection of Enteric Viruses and a Human-Specific Bacteriological Marker.” Water Research 43, no. 4: 1137-1149.

## Acknowledgment

This work is supported by HATCH Project 1016068; NIFA 2019-67021-29936 from the USDA National Institute of Food and Agriculture.

# Authors

Biological & Agricultural Engineering
Assistant Professor
Biological & Agricultural Engineering
Postdoctoral Research Scholar
Biological & Agricultural Engineering
Assistant Professor
Biological & Agricultural Engineering
Associate Professor and Director, Marine Aquaculture Research Center
Biological & Agricultural Engineering