2019 SWCS International Annual Conference July 28-31, 2019 Pittsburgh, Pennsylvania

1. July 28 - 31, 2019
Pittsburg, Pennsylvania, USA
Assessing the Variability of E. coli in streambed Sediment and its Attachment
Rate to Sediment Particles and Monitoring the Effect of Seasonal Riparian
Area Management (SRAM)
Sadia Salam¹*, Rachel McDaniel¹ and Bruce Bleakley²
¹Department of Agricultural and Biosystems Engineering, SDSU;
²Department of Biology and Microbiology, SDSU

2. BACKGROUND
2
Figure 1: Sources and survival of bacteria in the water column*
*ImageCourtesy:UniversityofMaryland,CenterforEnvironmentalScience

3. Figure 2: Different phase of bacterial attachment to sediment particles*
*https://doi.org/10.1002/2013JC008919
3

4. *https://iaspub.epa.gov/waters10/attains_state.control?p_state=SD&p_cycle=2016
 United States Environmental Protection Agency (US EPA) reported FIB such as E. coli is
the prime reason for poor water quality in waterbodies in South Dakota.
Figure 3: US EPA water quality monitoring report
4

5. 5
Figure 4: A typical sketch of livestock fencing from the
stream which is the Seasonal Riparian Area
Management (SRAM) BMP
 South Dakota
Department of
Environmental and
Natural Resources (SD
DENR)
 Clean water act
 Central Big Sioux
watershed
 Farmers are getting
paid ($60/acre/annual)
Best Management Practice (BMP)

6. ▪ Monitoring SRAM in an
agricultural sub-watershed
▪ Assessing FIB (i.e E. coli)
attachment rate to sediment
particles
RESEARCH OBJECTIVES
6

7. 7
MATERIALS & METHODS
Figure 5: The four monitoring sites at Skunk Creek (Sk), a tributary of Big Sioux
Watershed located in Eastern South Dakota
Sk1 is the U/S site.
Cattle has direct access
Sk2, Sk3 and Sk4 are under
SRAM. Sk4 is the D/S site
Study Site

8.  For E. coli variability, collected total 25 sediment samples/site
 For seasonal variability, Three to Five sediment samples were collected from
May to October for two years
8
Monitoring SRAM
A1
A2
A5
A4
A3
B2
E1 D1 C1 B1
C2
B5
B4
B3
D2
C5
C4
C3
E2
D5
D4
D3
E5
E4
E3
Figure 6: 25 gridded (5X5) location for
Sediment sample collection
FLOW

9.  Used 1:11 and 1:10 dilutions
 Phosphate buffer solution
 Used the supernatant for samples processing
9
Figure 7: 50ml supernatant in a sterile tube for sample processing
Sample processing

10. 10
Sedimentation Procedure
Attached fraction = Total
counts - Unattached fraction
Attachment Rate
Unattached Fraction
Attached Fraction
Figure 8: Sedimentation method
for determining attachment rate of
E. coli to sediment particles

11. E. coli Enumeration
11
Figure 9: (a) Membrane Filtration Procedure, (b) purple E. coli
colonies grown in m-TEC plate
(a) (b)

12. 12
 E. Coli Variability
Figure 10: Boxplot showing E. coli variability in four monitoring sites. E. coli variability
is decreasing gradually from site Sk1 to site Sk4.
Site variability
RESULTS AND DISCUSSIONS

13. 13
Location Edge ÷
Middle
Sk1 0
Sk2 3
Sk3 2
Sk4 2
Table 1: The ratio between the edge
and middle of the stream calculated
from the mean E. coli concentrations
107
263
194
67
329
89
116
31
0 50 100 150 200 250 300 350
Sk1
Sk2
Sk3
Sk4
Sediment E. coli concentration in CFU/ g
Middle of the stream Edge of the stream
Figure 11: E. coli concentration of sediment
sample on the edge and middle of the stream in
CFU/ g.

14. 14
Figure 12: Seasonal variation of median E. coli concentration from May to October for Sk1, Sk2,
Sk3 and Sk4 in (a) year 2017 and (b) year 2018. The white area in 2018 indicates no data available.
For better visual representation, log-transformed data was used (log10 CFU/ g).
(a) (b)
Seasonal variability

15. Year
Streamflow (ft3/s)
Mean Early
Season
Late Season
May -
October May - July
August -
October
2017 18.5 33.4 3.5
2018 151.7 104.7 78.4
folds (~) 8 3 22
15
Table 2: USGS* 06481480 station mean streamflow(ft3/s) data in
year 2017 and 2018. The six months data divided into two season
early season and late season for explaining E. coli in stream both
in water and sediment.
*https://climate.sdstate.edu/
*United States Geological Survey

16. 16
14.4
179
6.77 3.284.14
12.3
1.83 DNQ
0
40
80
120
160
200
Sk1 Sk2 Sk3 Sk4
MarkerQuantified
(×10³copies/g) General
Bacteroidetes
Ruminant Fecal
 Microbial Source Tracking (MST)
Figure 13: Microbial Source tracking number for “General Bacteroidetes” and “Ruminant
Fecal” in all four monitoring sites. Here, “DNQ” abbreviated from ‘Do Not Quantified’.
 Real-time quantitative Polymerase Chain Reaction (qPCR) DNA analytical technology

17. Monitoring
Site
D50
(mm)
Correlation factor,
R
< 0.075
mm > 2 mm
Sk1 0.35 -0.56 0.3
Sk2 0.32 0.62 -0.6
Sk3 0.34 -0.48 0.35
Sk4 0.34 0.7 -0.2
17
Table 3: D50 value of sediment particle
size and Spearman correlation
coefficient for both fine (<0.075 mm)
and coarse (> 2mm) particles.
Site
Mean
(%)
Geo-mean
(%)
Correlation
coefficient
Sk1 4.8 2.9 -0.13
Sk2 4.2 2.6 0.2
Sk3 8.2 5.3 0.03
Sk4 6.1 4.2 -0.04
Table 4: Mean and Geo-mean of %
organic content for 6 months of
recreational period (May to October)
with its correlation with E. coli
concentration in sediment
 Sediment Physical Characteristics
* Sieve analysis test
* Loss on ignition

18. 18
Antibiotic Resistance Test (ABR)
0
20
40
60
80
100
Penicillin Ampicillin Erythromycin Tetracycline Sulfisoxazole
%ResistantofE.coliisolates
Sk1 Sk2
Antibiotics p-value
Penicillin 0.12
Ampicillin 2.20E-16
Erythromycin 0.23
Tetracycline 0.78
Sulfisoxazole 3.90E-13
Table 5: Significance
difference between Sk1
(cattle access) and Sk2
(under SRAM)
Figure 14: ABR test result for selected E. coli isolates
of site Sk1 (cattle access) and Sk2 (under SRAM)
Total isolates = 463
* Modified Kirby-Bauer method

19. 19
Table 6: % E. coli (Average ±
Standard Deviation) association
with sediment particles
Figure 15: Average attachment rate (%) in four
monitoring sites for sediment samples
Attachment Rate
Site September October
Sk1 59 ± 10.3 -
Sk2 64 ± 10.5 70 ± 13.4
Sk3 51 ± 33.3 78 ± 14.1
Sk4 37 ± 33.7 49 ± 34.2
59
64
51
37
N/A
70
78
49
0
20
40
60
80
100
Sk1 Sk2 Sk3 Sk4
AttachmentRate(%)
September
October

20. 20
Figure 16: Average attachment rate (%) during sample collection
for both water* and sediment samples.
*Amegblator, L., 2018. Evaluating E. coli Particle Attachment and the Impact on Transport during High Flows. South Dakota State University. M.Sc. Thesis, South Dakota State University.
58
19
42
70
0
10
20
30
40
50
60
70
80
Sediment Water Sediment Water
Attached Unattached
%Attached
Average (%)

21. SUMMARY
Highest FIB observed at cattle crossing site Sk1
All 3 sites under SRAM indicates positive impact on
reducing FIB
Both MST* and ABR test result showed highest marker
quantification and higher antibiotics resistance observed at Sk2
21
*limited data to make conclusion

22.  Strong correlation with sediment particles and
preliminary data of attachment rate observed during
baseflow condition
▪ Above results point out that microbial fate and
transport contribution on water quality impairment
22

23. FUTURE WORK
▪ Assess more sediment samples for attachment rate of FIB
to sediment particles
▪ Estimate the shear stress to understand the effect of stream
channel dimensions on FIB reservoir
23

24. ACKNOWLEDGMENTS
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 South Dakota Department of
Environment and Natural
Resources (SD DENR)
 United States Department of
Agriculture (USDA) Hatch
Project SD00H604-15

25. 25
Sadia Salam
sadia.salam@sdstate.edu
https://www.linkedin.com/in/sadiabsalam/
Rachel McDaniel
rachel.mcdaniel@sdstate.edu
https://www.mcdanielwaterresearch.com
QUESTION/s?