# Presentation: 345095

## Sources and Variability of Cryptosporidium in the Milwaukee River Watershed: An Application of Automatic Sampling for Pathogens

- Sources and Variability of Cryptosporidium in the Milwaukee River Watershed: *An Application of Automatic Sampling for Pathogens*

- Steve Corsi, Rob Waschbusch, John Walker, Jon Standridge
- Cooperator: Water Environment Research Foundation

## Introduction

- The public has come to expect a safe source of drinking water
- 1993 *Cryptosporidium* outbreak in Milwaukee
- 403,000 people fell ill
- 150 died
- This incident made Waterborne Cryptosporidium an international water quality issue
- Original source of cryptosporidium *was not identified *
- Many sources exist in the Milwaukee River Watershed

## Objectives

- Define relative magnitude and variability of *Cryptosporidium *from major sources
- Urban land use
- Agricultural land use
- Public wastewater discharges (POTW)
- Characterize contributions by environmental factors
- Hydrograph timing
- Climatic effects
- Seasonal variations
- Develop a model to predict the probability of high Cryptosporidium concentrations

## Milwaukee River Watershed

- Milwaukee River Watershed

- Monitoring Locations

## Crypto Sampling Approach

- 2 years of monitoring
- Fixed-interval sampling: every 4 weeks
- Event sampling: precipitation and snowmelt runoff events
- 2 events sampled per season each year at subwatershed sites
- 6 months per POTW (3-4 events + FI)

## Automatic sampling Techniques: Pros and Cons

- Pros
- Unattended Sampling
- Less storm chasing (***a better nights sleep***)
- Flow weighted or time based options
- Hydrograph coverage
- Represent short term variability
- Long-term composite
- Loading computations (Relative source contributions)
- Event-triggered sample collection
- Flow, turbidity, rain, . . .
- Telemetry for remote access
- Reduced labor

- Cons
- Start-up equipment and installation costs
- Fixed location for equipment
- Point samples rather than integrated over cross section
- Some additional QA/QC requirements

## Monitoring Station Schematic

**Monitoring Station Schematic**

## Additional Instrumentation for other studies

- Meteorological sensors (wind, solar radiation, rainfall)
- Wave height
- Fluorometer for dye tracer studies
- Many other sensors . . .
- All can be interfaced to datalogger system
- Used to trigger sampling
- Used to complement other data in final analysis/modeling

## Monitoring Station

## Extra Details for “Clean” Sampling

## Two years of Data Collection

*Cryptosporidium* concentrations
- Streamflow
- Precipitation
- Turbidity
- Land-use data
- Water temperature, pH, conductivity (Underwood Creek)

## Cedar Creek Sampling Periods

## Underwood Creek Sampling Periods

## Event Sampling Based onHydrograph Position

## Results

- Data preparation (Censored data estimates)
- Exploratory data analysis
- Site by site comparison
- Event loadings
- Effect of hydrograph position
- Interevent variability
- Seasonality of data
- Probabilistic regression model

## Percent Occurrence

## Censored data analysis

- Using simple substitution can bias the data
- For this project
- Estimated values for censored data
- Assumption of a single statistical distribution for each site (lognormal)
- Procedures defined by Helsel and Cohn (Water Resources Research, 1988, Vol 24, No 12, p. 1997-2004)

## Censored Value Estimates: Underwood Creek

**Censored Value Estimates: Underwood Creek**

## Site by Site Comparison:Concentrations

**1**

**10**

**100**

**1000**

**10000**

**100000**

- 78

- 28

- 71

- 28

- 16

- 9

- x

- x

- x

- o

**Event     FI**
**Cedar Creek**

**Event    FI**
**Underwood **
**Creek**

**Event    FI**
**POTW #1**

***Cryptosporidium***** (oocysts/100L)**

## Event loadings: Cryptosporidium Unit Area Event Loads

**1.0**

**10**

**100**

**1000**

- 16

- 16

**Million Cryptosporidium **
**per square mile**

**Cedar          Underwood **
**Creek               Creek**

## Hydrograph Position: Underwood Creek

**1**

**10**

**100**

**1000**

**10000**

**100000**

- 14

- 16

- 21

- 16

- 32

- o

**Base  First   Rise  Peak  Falling**
**Flow  Flush   **

**Cryptosporidium (oocysts/100-L)**

## Hydrograph position at Other Sites

- No differences were detected due to hydrograph position at
- Cedar Creek
- POTW #1

## Cedar Creek: Cryptosporidium Concentration for Individual Runoff Events 

## Underwood Creek: Cryptosporidium Concentration for individual runoff Events 

## POTW #1: Cryptosporidium Concentration for individual runoff Events

**POTW #1: Cryptosporidium Concentration for individual runoff Events**

**                ****2                   3**

## Seasonal effects

- Cedar creek had slightly higher concentrations in the summer (p<0.05)
- Underwood Creek and POTW sites did not show a seasonal trend
- No general conclusions on seasonality can be made from this study

## Seasonal Effects From Previous Studies

- Greater concentrations during:
- Fall and early spring (LeChevallier et al. 2002)
- Fall and winter (Bodley-Tickell et al. 2002)
- Winter and summer (Rouquet et al., 2000)
- Winter and spring (Atherholt et al. 1998)

## Statistical Model

- Discriminant function analysis
- To help determine which variables may explain variability
- Logistic Regression
- To predict the probability of a high *Cryptosporidium* concentration
- Performed on
- combination sites
- Individual sites
- Baseflow samples
- Event samples

## Independent Variables Used in Statistical Analysis

- Flow
- Turbidity
- Precipitation depth
- Precipitation intensity (max 5, 15, and 30 min)
- Antecedent precipitation (1, 3, 5 day)
- USLE erosivity index
- Hydrograph position
- Water temperature, pH, conductance (Underwood Creek)
- Annually cyclical term for seasonality: Cos(T), Sin(T)

## Logistic Regression Results

- Two different equations were most successful in predicting high  *Cryptosporidium* concentrations (>300 oocysts/100L)
- Baseflow periods for both Cedar and Underwood Creeks
- Event periods for Cedar and Underwood Creeks

## Baseflow Periods

- Probability of high cryptosporidium increases with increasing
- Turbidity
- Antecedent precipitation

## Event periods

- Probability of high concentrations increases
- Precipitation intensity
- Antecedent precipitation
- During November and January
- Probability of high concentrations decreases on
- Falling limb of hydrograph

## Prediction Accuracy for High Cryptosporidium Concentraions (>300 oocyst/100L)

| Sample type |  | a-priori probability | Correct positive | False negative |
| --- | --- | --- | --- | --- |
| Baseflow |  | .09 | 1.00 | 0.00 |
| Event |  | .16 | 0.45 | 0.08 |

## Conclusions

- Concentrations and Unit Area Loads were greater at the urban site than the rural or POTW sites

- Fixed interval sampling did not represent the overall magnitude and variability
- event samples (more than one) are necessary

- Concentrations in subwatersheds were greater during runoff periods than during baseflow periods

## Conclusions (Continued)

- Discriminant function analysis and logistic regressions can provide predictions of the occurrence of high concentrations

## Daze