Sources and Variability of Cryptosporidium in the Milwaukee River Watershed: An Application of Autom

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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
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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

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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

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Milwaukee River Watershed
Monitoring Locations
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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)

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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

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Monitoring Station Schematic
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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

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Monitoring Station
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Extra Details for Clean Sampling
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Two years of Data Collection

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

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Cedar Creek Sampling Periods
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Underwood Creek Sampling Periods
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Event Sampling Based onHydrograph Position
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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

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Percent Occurrence
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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)

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Censored Value Estimates Underwood Creek
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Site by Site ComparisonConcentrations
100000
78
28
71
28
16
9
o
10000
x
1000
x
x
Cryptosporidium (oocysts/100L)
100
10
Event FI Cedar Creek
Event FI Underwood Creek
Event FI POTW 1
1
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Event loadings Cryptosporidium Unit Area Event
Loads
16
16
1000
100
Million Cryptosporidium per square mile
10
1.0
Cedar Underwood Creek
Creek
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Hydrograph Position Underwood Creek
100000
14
16
21
16
32
o
10000
1000
Cryptosporidium (oocysts/100-L)
100
10
1
Base First Rise Peak Falling Flow Flush
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Hydrograph position at Other Sites

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

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Cedar Creek Cryptosporidium Concentration for
Individual Runoff Events
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Underwood Creek Cryptosporidium Concentration
for individual runoff Events
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POTW 1 Cryptosporidium Concentration for
individual runoff Events

• 2 3

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Seasonal effects

• Cedar creek had slightly higher concentrations in
  the summer (plt0.05)
• Underwood Creek and POTW sites did not show a
  seasonal trend
• No general conclusions on seasonality can be made
  from this study

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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)

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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

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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)

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Logistic Regression Results

• Two different equations were most successful in
  predicting high Cryptosporidium concentrations
  (gt300 oocysts/100L)
• Baseflow periods for both Cedar and Underwood
  Creeks
• Event periods for Cedar and Underwood Creeks

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Baseflow Periods

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

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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

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Prediction Accuracy for High Cryptosporidium
Concentraions (gt300 oocyst/100L)
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Conclusions

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

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

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

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Conclusions (Continued)

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

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Daze