1. Rapid assessments of recreational water quality
River Rally
May 4, 2008
Donna Francy, Amie Brady, and Rebecca Bushon
USGS Ohio Water Science Center

4. Today’s Agenda
Introduction to public health microbiology and bacterial indicators
Recreational water quality monitoring and assessment
Rapid analytical methods
Predictive models

5. Waterborne disease is a public health concern
Pathogens ingested from
drinking water
recreational water
contaminated fish or shellfish
Potential sources of pathogens
treated and untreated sewage
septic tanks
combined sewer overflows
landfills
animal waste

6. Incidence of recreational waterborne disease outbreaks in the US
Associated Illness
Incidence
Gastroenteritis
30 (48.4%)
Dermatitis
13 (21.0%)
Acute Respiratory Illness
7 (11.3%)
Others:
Amebic Meningoencephalitis, Meningitis, Leptospirosis, Otitis Externa, Mixed Illnesses
12 (19.3%)
Etiologic Agents Identified:
Bacteria 32.3%
Protozoa 24.2%
Virus 9.7%
Chemical or Toxin 4.8%
Centers for Disease Control and Prevention (CDCP)

7. Factors effecting incidence of waterborne Illness in the US
Increasingly greater threat to public health
Increase in population
Aging water-treatment systems
Aging population
Inadequately managed animal wastes
Lack of integrated regulatory approach

8. Types of waterborne pathogens
Protozoa
(larger, complex cells)
Viruses
(tiny, non-living)
Bacteria
(medium size, simple cells)

9. Pathogens and swimming-associated illnesses
Disease
Cryptosporidium parvum
Cryptosporidiosis
Giardia lamblia
Diarrhea
Naegleria fowleri
Headache, fever, and vomiting
Campylobacter jejuni
Gastroenteritis
E. coli pathogenic strains
Salmonella, Shigella, and Vibrio species
Various enteric fevers, gastroenteritis, cholera, dysentery, septicemia
Adenovirus
Respiratory and gastrointestinal infections
Hepatitis
Infectious hepatitis (liver malfunction); also may affect kidneys and spleen
Norovirus

10. Why don’t we test directly for waterborne pathogens?
Safety
May require direct manipulation of pathogenic organisms
Time and cost
Each pathogen must be detected using a different test
Requires processing of large volumes of sample
Pathogens usually are present in low concentrations

11. Indicators of fecal contamination
Indicator organisms
usually NOT pathogenic
"indicate" the possible presence of pathogenic organisms
Used to directly detect the presence of fecal contamination from warm-blooded animals
E. coli

12. An Ideal Indicator Organism
Has an easy testing procedure
Is of human or animal fecal origin
Survives as long as, or longer, than pathogens
Present at densities related to the degree of fecal contamination
Is a "surrogate" for many different pathogens
Useful in fresh and saline waters
Enterococci

13. Indicator Organisms—Problems
Present when there is no fecal contamination
Total coliforms and C. perfringens are found in soil so they are not exclusively indicators of fecal contamination
Absent when pathogens are present
E. coli may die off faster than viral pathogens
Density may not always relate well to the density of pathogens
E. coli can reproduce in warm, tropical waters

14. Methods for detecting indicators
Membrane
filtration
method

15. Methods for detecting indicators
Enzyme substrate
tests

16. Tests for Escherichia coli
Membrane filtration method
Modified mTEC agar
E. coli colonies are magenta colored after incubation.

17. Tests for Escherichia coli
Enzyme substrate test
Colilert
E. coli positive wells fluoresce under UV light.
(Total coliforms positive wells are yellow under ambient light.)

18. Membrane filtration method Enterococci colonies have a blue halo.
Tests for Enterococci
Membrane filtration method
mEI agar
Enterococci colonies have a blue halo.

19. Enterococci positive wells fluoresce blue under UV light.
Tests for Enterococci
Enzyme substrate test
Enterolert
Enterococci positive wells fluoresce blue under UV light.

20. Recreational water quality monitoring and assessment

21. Definitions Criteria Are not rules and do not have regulatory impact.
Scientific data and guidance on the potential human health risk involved in the water’s use (or in acceptable limits for aquatic life).

22. Definitions Standards Have regulatory impact
Rules set forth by the state or USEPA to protect users of waters (based on water-quality criteria)
For indicator bacteria, based on a quantifiable relation between
density of the indicator in water
the potential human health risk by the water’s use

23. Criteria for recreational waters
Relation between E. coli and swimming-associated gastrointestinal illness

24. Criteria for recreational waters
Relation between fecal coliforms and swimming-associated gastrointestinal illness

25. USEPA criteria for recreational waters—1986

26. USEPA BEACH PROGRAM
Beaches Environmental Assessment and Coastal Health Act of 2000 (BEACH)
Required the states to adopt criteria into standards
Required USEPA to develop new criteria by late 2005
Required USEPA to address research topics such as modeling/monitoring, exposure and health effects
Provided grants to the states and local governments to develop new monitoring programs
Provided national beach guidance

27. Need for rapid assessments
At Ohio beaches, advisories are issued if E. coli is > 235
Culture results take hours
Water quality may change during that time

28. Solutions for rapid assessments
Rapid analytical methods
Description of qPCR and IMS/ATP
Rapid method studies
Predictive models
State of the science
Nowcasting at Ohio beaches and a recreational river

29. SURFACE RECOGNITION (IMS/ATP)
SOLUTIONS?
RAPID ANALYTICAL METHODS
MOLECULAR (qPCR)
SURFACE RECOGNITION (IMS/ATP)

30. qPCR RAPID METHOD Target and amplify specific DNA sequences
Standard curve is created from analyzing known quantities of target organism
Unknown sample values are interpolated from the standard curve
Unknowns

31. Field Testing of qPCR Method
USGS and Northeast Ohio Regional Sewer District
Samples were collected from July – September and 2007 at two Lake Erie beaches - Edgewater and Villa Angela
Objective: Compare results obtained by qPCR to those of the conventional membrane-filtration method
Project funded by the Ohio Department of Health

32. E. coli Standard Curve
y = x
R2 =

33. Correlations between qPCR and membrane filtration for E. coli

34. qPCR results – Villa Angela 2007
Predictive
tool
Sample size
Correct
Sensitivity
Specificity
qPCR 38
87%
(33/38)
91%
(21/23)
80%
(12/15)
Previous day’s E. coli 33
73%
(24/33)
70%
(14/20)
77%
(10/13)

35. Next Steps for qPCR DNA extraction Test different extraction kits
Analyze samples daily or weekly – not in batch format
qPCR
Find alternate source for assay reagents
Determine the best data interpretation procedure
Transfer technology to local agencies

36. IMS/ATP RAPID METHOD Immunomagnetic separation (IMS)
Uses antibody-coated magnetic beads which bind to antigens present on the surface of cells
Adenosine triphosphate (ATP)
Energy molecule in all cells
Reported in Relative Light Units (RLU) M A G N E T

37. Field Testing and Technology Transfer of IMS/ATP Method
Water samples from Ohio Lake Erie beaches
In cooperation with local and state cooperators ( )
Water samples from a recreational river (CVNP)
In cooperation with federal cooperators ( )
Sewage samples from Ohio, NC, and CA plants and water samples from Avalon Beach, CA
In cooperation with Southern California Coastal Water Research Project (SCCRWP)

38. Correlations between IMS/ATP and membrane filtration for E. coli
Cuyahoga River at Jaite, 2006
r=0.55
r=0.67
r=0.89

40. Villa Angela 2007: Relations to E. coli
Early Summer
Late Summer
IMS/ATP method
r = 0.49
p =
r =
p =
Turbidity
r = 0.40
p =
r = 0.25
p =
Number of birds
r = 0.41
p =
r = 0.15
p =

41. Next steps for IMS/ATP method
Continue refinement of IMS/ATP
Identify additional antibodies that include most strains
Optimize the beads and reagents
Test at other locations
Test whether it is a stand alone method or can be used in existing models, integrate in predictive models
Epidemiological study - SCCWRP
Transfer technology to local agencies

42. SOLUTIONS? PREDICTIVE MODELS
RAINFALL BASED ALERTS
Stamford, Ct (20 years)
Door County and Milwaukee, Wi
Southern Ca (10 years)
Delaware (12 sites)
Myrtle Beach, SC
Boston Harbor
Ozaukee County, Wis
(may use in 2008)

43. MULTI-VARIABLE STATISTICAL MODELS
SOLUTIONS?
MULTI-VARIABLE STATISTICAL MODELS
Linear relations between variables and E. coli
Use statistical techniques such as multiple linear regression
Beach specific models
Does not require identification of the source
r=0.56
P<0.0001

44. SOLUTIONS? STATISTICAL MODELS OPERATIONAL MODELS
Project SAFE, IN (4 beaches, 3 yrs)
SwimCast, Lake County, IL (3 beaches, 1−3 yrs)
Chicago, (2 beaches, begin in 2008?)
Nowcast, Ohio, (1 beach, 2 years)

45. NOWCASTING AT OHIO BEACHES
Rainfall
Turbidity
Wave height
Lake level
Water temp
Wind direction
Day of the year

46. NOWCASTING AT OHIO BEACHES Threshold probability ranges from 27 to 32%
Huntington and Edgewater
Wave height
Turbidity
48 hr weighted rainfall (Airport)
Antecedent dry days
Radar rainfall
Day of the year
Lake level
Huntington, Bay Village
Output from the model is the probability that E. coli will be >235 CFU/100 mL
Threshold probability ranges from 27 to 32%

47. Edgewater wave height buoy

48. Ohionowcast.info

49. NOWCAST results in 2007 Model 78 82.1% 50% (11/22) 94.6% 66 66.7%
Edgewater, Cleveland, Ohio
Predictive tool
Sample size
Correct
Sensitivity
Specificity
Model 78
82.1%
50%
(11/22)
94.6%
Previous day’s E. coli 66
66.7%
33.3%
(6/18)
79.2%

50. Next steps for NOWCAST Villa Angela and Lakeshore, Ohio
Standard was exceeded on the majority of days tested
Correct responses—Model 61-63%, Current method %
Consider using QPCR or IMS/ATP
Huntington and Edgewater
Improve performance of the model
Enable real-time measurements

51. Cuyahoga Valley National Park
Cuyahoga River

52. Develop predictive models
IMS/ATP rapid method results
Streamflow
Turbidity
Rainfall

53. Environmental variables in relation to E. coli concentrations
Pearson’s r correlation coefficients (number of samples)
Year
IMS/ATP rapid method
Rain*
Estimated
Discharge
Log10 turbidity
2004-5
0.55 (78)
0.56 (140)
0.43 (125)
0.78 (141)
2004-6
0.61 (118)
0.59 (179)
0.56 (164)
0.82 (180)
*Rainfall data gathered from the Automated Flood Warning System:

54. CVNP – Turbidity model 2007 Data: Model results vs.
Actual concentrations

55. CVNP – Turbidity model Jaite – 2007 data Predictive tool Sample size
Correct
Sensitivity
Specificity
Model 31
81%
94%
64%
Previous day’s E. coli 26
68%
62%
83%
Cuyahoga Valley N.P. Resource Management Office and Lab

56. Next Steps - CVNP Funded projects: Continue data collection in 2008
Implement the models to the public
Continue data collection in
Post model results on NOWCAST website
Outreach – news releases, factsheet
Transfer technology to NPS
Other:
Use of real-time turbidity sensor at site

58. Acknowledgements
Christopher Kephart, Erin Bertke, Robert Darner, USGS Ohio Water Science Center
Jill Lis, Cuyahoga County Board of Health
Suzanne Britt, Ann Gliha, and Patricia Boone, Cuyahoga County Sanitary Engineers
Eva Hatvani, Mark Citriglia, Lester Stumpe, NEORSD
Mark Pfister, Lake County Health Department, IL
Richard Whitman, USGS, Porter, IN
Calum McPhail, Scottish Environmental Protection Agency
Meg Plona, Cuyahoga Valley National Park
Ohio Lake Erie Commission
Ohio Water Development Authority