Transcript Fecal Colform Bacteria Contamination during Rain Events in Sayler’s Creek, Virginia Blake N.

Fecal Colform Bacteria
Contamination during Rain Events
in Sayler’s Creek, Virginia
Blake N. Robertson
Senior Honors Research
Under the Supervision of
Dr. David Buckalew
Natural Science Department
Longwood University
Categories of River and Stream
Impairment:
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Suspended Sediments
Biochemical Oxygen Demand
Nutrients
Toxic Chemicals
Heavy metals
Fecal Coliform Bacteria
Magnitude of Impairment
• 13,218 miles of streams and rivers monitored
in Virginia by Virginia’s Department of
Environmental Quality
• 52% or 6,301 of those miles were determined
to be impaired (DEQ 2004 303(d) and 305 (b))
Why Focus on Bacterial Pollutants?
“Agricultural to be one of the primary sources
Contributing to the bacteria standards violations (DEQ)”
TMDL plans
•The Clean Water Act requires states to establish
water quality standards
•Water quality is determined by ability to support
specific uses
•If water quality is not sufficient, then a Total
Maximum Daily Load plan is created
•In Virginia, TMDL plans are optional
•However, some aid exists
Uses and their corresponding fecal coliform bacteria standard
Drinking Water
1 colony forming unit (CFU) per
100 ml
Total body contact
200 CFU/100ml
Partial Body contact
1000 CFU/100ml
Treated sewage
effluent
<200 CFU/100ml
Why Focus on Sayler’s Creek?
• Impaired headwater of
the Appomattox River
• Drains into the heavily
depended upon
Chesapeake Bay
• Public Health
– Local and Regional
• Eutrophication is
accelerated leading to
anoxia
• Lack of data
Past and Ongoing Studies
• Clean Virginia Waterways ARWQMP
samples monthly at four locations in the
Sayler’s Creek watershed
• Virginia’s DEQ monitors monthly where
the two tributaries in the watershed meet
• Scarcity of research existing for the area
• Excellent place for such a study
Objectives
Objective: To quantify fecal coliform bacterial
contamination entering the stream during rain events
Secondary Objective: To measure differences
between testing methods for fecal coliform bacteria
in water samples collected during rain events
Experimental Hypotheses
Hypotheses:
H0: An increase in streamflow will not cause fecal
coliform contamination to increase
HA: An increase in streamflow will cause fecal
coliform contamination to increase
Hypotheses:
H0: There is no difference in coliform
contamination between sampled sites
Hypotheses:
H0: Measures of coliform bacteria do not
differ between testing methods
HA: There is a difference in coliform
contamination between sampled sites
HA: Measures of coliform bacteria differ
between testing methods
Field Data Collection
Sample Collection
• Water samples collected before (baseline), during, and
after rain events
• Duplicate water samples taken randomly
Site Characterization
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Precipitation for event period
Water velocity
Water temperature
Water depth
Information not collected in every instance
Determining Stream Profile
Establishing a Transect
Surveying the Stream Profile
Measuring Streamflow
Stream Profile Example
Salyer 7 Stream Profile
1
98
0
97
-1
96
-2
95
Distance from left bankfull (Feet)
18
17
16
15
13
11
9
7
5
3.
3
2
-3
94
0
Depth (Feet)
2
99
Discharge Calculation
Discharge or Q (ft3/sec) = velocity (ft/sec) X stream area (ft2)
Example:
Stream Area = 5.558 ft2
Water Velocity = 0.713 ft/sec
Discharge = 3.964 ft3/sec
Sample Collection
• Samples were collected according to
published guidelines of the Standard
Methods for the Examination of Water and
Wastewater, 20th ed.
• Samples were collected in sterile 18oz.
Whirl-Pak Bags
• Stored in a cooler with ice packs
• Transported to the lab for processing
within 6 hours of sample collection
Sample Collection
Samples Transported for
Processing
Membrane Filtration
• Samples diluted to 1% and
passed through a 0.45
micron Millipore filter
• Incubated at 44.5 +/- 0.5°C
for 24 +/- 2 hours.
Apparatus
mFC Agar
Defined Substrate
• Water sample is combined with
two substrates (ONPG and
MUG)
• Incubated at 44.5 +/- 0.5°C for
24 +/- 2 hours.
• Coliforms use ß-galactosidase
to metabolize ONPG and
change the cell to a yellow color
• E. Coli uses ß-glucuronidase
to metabolize MUG and create
fluorescence
Site Identification
Little Sayler’s Creek
•Sayler’s 6
- 37 17’ 22’’ N and 78 16’ 22 W
•More human land
use upstream
Big Sayler’s Creek
• Sayler’s 7
– 37 18’ 29’’ N and 78 13’ 41’’ W
• More forested land
upstream
•Both are 2nd order streams (Headwaters)
•Drain a similar amount of land
•Similar bottom substrate and riparian
buffer at each site
Statistical tests
• To determine if any statistical differences exist
– Confidence Level = 95%
• Normality was tested for each data set
– Bacterial assays
– Assay method
• Tests used include:
– A paired-sample t-test was used to compare methods
of measuring fecal coliform bacteria
– A nonparametric, related samples test to compare
baseflow bacterial counts with those of peak flow
– A Wilcoxon Rank Sum test was used to compare flow
and bacterial counts between the two different sites
Results: Site Characteristics
Location
Saylers 6
Sayler 7
Temperature
Avg (Sd, n)
Velocity
Avg
(ft/ sec)
Discharge
Avg
(ft3/sec)
62.45 F°
(2.27, 42)
63.39 F°
(2.47, 42)
1.23
10.71
1.23
9.65
•Precipitation data was not used for
rain events 4 through 7
Results
ra
in
7
6
5
4
3
2
1
12000
10000
8000
6000
4000
2000
0
ev
en
t
CFU's per 100ml
Mean
Contamination
of Rain Events
Mean
Bacterial
Counts for Sampled
Rain Events
Sayler 6
Sayler 7
Saylers 6 had a greater mean bacterial count over
the 7 rain events
Rain Event at Sayler 6
30000
3.5
3
2.5
Velocity (ft/sec)
20000
2
15000
1.5
10000
1
5000
• Sampled rain event:
September 4th to 8th
0.5
0
0
10
20
30
40
Total Hours Elapsed
•At sampled times,
contamination appears to
increase and decrease
with streamflow at both
sites
•Similar trend appears for
each rain event
Rain Event at Sayler 7
30000
3
25000
2.5
20000
2
15000
1.5
10000
1
5000
0.5
0
0
0
10
20
Total Hours Elapsed
30
40
Velocity (ft/sec)
0
CFU's per 100/ml
CFU's per 100ml
25000
Streamflow and
Bacterial Count
Statistical Results
Variable
Sig.
Result
P<0.002
Reject H0
Velocity
P>0.88
Fail to reject H0
Discharge
P>0.28
Fail to reject H0
Fecal Coliform
P<0.005
Reject H0
Site Comparison Fecal Coliform
Peak and
Baseline
Comparison
Summary of Statistics
Conclude that there was no difference between velocity and
discharge at the two sites during sampled rain events.
Conclude that the two sites differed in bacterial counts.
Conclude that there was a significant difference between peak
and baseline bacterial counts during sampled rain events.
Duration Since Last Rain Event
• At Sayler 6, on Little Sayler’s Creek, a rain
events with similar amounts of rain yielded
different concentrations of fecal coliform
bacteria.
Rain Event Number
1
3
Peak Stream Flow (ft/sec)
>3.0
>3.9
Time since last rain event
14 days
4 days
Peak Contamination (CFU’s per 100ml)
28,500
4,800
Total precipitation (in)
2.35
2.5
Method Comparison
Variable
Sig.
Result
Method
Difference in fecal
P>0.9 Fail to reject
7
H0
comparison Coliform measurements
Summary of Statistics
Conclude that there was no difference
between the methods for measuring
fecal coliform bacteria during rain
events at the two sampled sites.
Summary of Results
• There is a difference in bacterial counts between
sites sampled.
– Sayler 6 is more heavily contaminated
• When both sites were included, there was a
difference between peak flow and baseflow fecal
coliform contamination.
• A trend between streamflow and fecal coliform
contamination exists.
• There was no difference in the methods used to
test for fecal coliform bacteria.
Future Studies and Recommendations
• The DEQ had listed the cause of fecal coliform pollutants
as agricultural, but the source is now listed as ‘unknown’.
– Stresses the importance of bacterial source tracking
• Adopt a TMDL plan.
• Determine if the area upstream of Sayler 6 that drains into
Little Sayler’s Creek is more developed than the area
above Big Sayler’s Creek and how that is affecting bacteria
counts.
• Investigate the effect of varying times between rain events.
• The Sayler’s Creek watershed is an excellent outdoor
classroom.
Acknowledgements
•I would first like to acknowledge and thank Dr. David
Buckalew for the supervision and guidance he
provided. I learned a great deal this year and owe
much of it to him.
•Thank you to Mrs. Alecia Daves of the Piedmont Soil
and Water Conservation District for her surveying
assistance.
•This research was funded by the Dean’s Fund for
Undergraduate Research