The document summarizes research to optimize the ripening period of slow sand filters. The objectives were to optimize the scraping procedure, accelerate biological activity, and identify indicators of ripening. Various column experiments were conducted with different filtration rates, added nutrients and inoculum. Results showed that biomass accumulated mainly in the top sand layers. Columns with higher rates and additions had faster reductions in turbidity, particles, and bacteria/virus removal, identifying them as potential ripening indicators. Chemical parameters like DOC and TN did not correlate as well with ripening.

1. Optimising the
Ripening period of
Slow Sand Filters
Hemant Arora
MSc Civil Engineering
(Water Management)

2. Access to Clean
Water
663 million people – one in 10 – still drink
water from unprotected sources.
2

3. Outline
Knowledge
Gaps
Objective
and
Approach
Materials &
Methodology
Results Conclusion
3

4. Knowledge gaps
Factors affecting
the startup time
(ripening period) of
SSF ?
What are the
Indicators of
ripening?
4

5. Main research question
How can we reduce the
ripening period of slow
sand filters and identify
the indicators of ripening
?
5

6. 6
Knowledge
Gaps
Objective
and
Approach
Materials &
Methodology
Results Conclusion

7. Strategy
7
Scraping
Procedure
Accelerate the
biological
activity
Optimise
the
Ripening
Indicators
of Ripening

8. Objective 1:
Optimise Scraping
Procedure
To optimize
the
Scarping
Procedure
Vertical
Biomass
Distribution
Spatial
Biomass
Distribution
Inactivation
potential
Biological
Activity
8

9. Objective 2:
Accelerate the
Biological Activity
To
accelerate
the
biological
activity
Increasing
the filtration
rate
Use of
Additional
nutrients
Use of
microbial
inoculum
9

10. Objective 3:
Indicators of
Ripening
10
To identify
the
of ripening
Chemical
Parameters
(Dissolved Organic
Carbon & Total
Nitrogen)
Microbial
Parameters
(Bacteria
Virus)
Physical
Parameters
(Turbidity &
Particle
Counts)

11. 11
Knowledge
Gaps
Objective
and
Approach
Materials &
Methodology
Results Conclusion

12. Experimental
Description
12
Two Full Scale Filters
8 Column Scale Filters
Filter Details Monster Katwijk
Depth of sand bed 90 cm 70 cm
Effective Sand size 0.35 mm 0.31 mm
Supernatant water level >1 m >1 m
Scraping done after 243 days 450 days
Area of filters 375 m² 850 m²
Filtration rate 35 to 40 cm/hr 25 to 40 cm/hr
Filter Details Monster
Depth of sand bed 60 cm (total)
Effective Sand size 0.30 mm
Supernatant water level >20 cm
Area of filters .00125 m²

13. 13
Variables
0.1 m/hr
Added
inoculum
(schmutzdecke
)
Additional
Nutrients
(5 * influent
value)
Reference
0.5 m/hr
Variables in Column Operation

14. Sand and Schmutzdecke
sample were analysed for:
1. Biomass concentration
by measuring ATP content
2. Cell counts using flow
cytometry
14
Methodology
Used:

15. 15
Sample Collection: Full Scale
Filter
(a) Katwijk (b) Monster
Schmutzdecke
Sand bed 0-2 cm
Sand bed 4-6 cm
Sand bed 8-10 cm

16. Water Quality Measurements
Spiking Experiments of Bacteria &
Viruses
16
Methodology used in Column SSF
Water Quality
Measurements
Turbidity
Particle
Counts
DOC
& TN
30 days
Regular
Twice a
WeekOnce a
Week
44 days
58 days
80 days
E.Coli WR1
E.Coli WR1
E.Coli WR1
MS2
0.1 NTU
200
Counts/ml
E.Coli WR1
MS2
E. Coli WR1 was used as a reference for
Bacteria.
MS2 bacteriophage as a reference for Viruses.

17. Inactivation Potential
17
Methodology used in Column SSF
90th
day
Removal of Schmutzdecke:
Spike of E.Coli WR1, MS2
91st
day
92nd
day
93rd
day
Removal of Sand bed 0-2 cm:
Spike of E.Coli WR1
Removal of Sand bed 4-6 cm:
Spike of E.Coli WR1
Removal of Sand bed 8-10 cm:
Spike E.Coli WR1
Schmutzdecke Sample:
Analysed for biomass and Cell Count
Sand Sample (4-6 cm):
Analysed for biomass and Cell Count
Sand Sample (8-10 cm):
Analysed for biomass and Cell Count
Sand Sample (0-2 cm):
Analysed for biomass and Cell Count

18. 18
Knowledge
Gaps
Objective and
Approach
Materials &
Methodology
Results Conclusion

19. Results: Objective 1
19
Optimise
Scraping
Procedure

20. 20
To optimize
the Scarping
Procedure
Vertical
Biomass
Distribution
Spatial
Biomass
Distribution
Inactivation
potential
Biological
Activity

21. FULL SCALE FILTERS COLUMN SSF
21
0
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
320
340
360
Schmutzdecke 0-2 cm 4-6 cm 8-10 cm
BiomassC(ng/g)
Sand Depth
Katwijk
Monster
0
20
40
60
80
100
120
140
160
180
200
220
240
260
280
300
320
340
360
Schmutzdecke 0-2 cm 4-6 cm 8-10 cm
BiomassC(ng/g)
Sand Depth
0.1 m/hr
0.5 m/hr
0.1 m/hr N
0.1 m/hr S
More than 80% of biomass in
Schmutzdecke and top 2 cm
Katwijk = 25 to 40 cm/hr
Monster = 35 to 40 cm/hr
Higher filtration rate pushes substrate
into deeper layers of sand bed

22. 22
To optimize
the Scarping
Procedure
Vertical
Biomass
Distribution
Spatial
Biomass
Distribution
Inactivation
potential
Biological
Activity

23. 23
Inactivation Potential
4.9
3.38
2.53
1.9
1.35
3.57
2.14
1.76
1.48
0.78
5.3
4.14
3.56
2.96
2.25
5.1
3.9
3.23
2.79
2
0
1
2
3
4
5
6
80th 91st 92nd 93rd 94th
DecimalEliminationCapacityE.Coli(LogRemoval)
Days
0.1 m/hr
0.5 m/hr
0.1 m/hr S
0.1 m/hr N
Removal of Sand bed
0-2 cm
Removal of Sand
bed
4- 6 cm
Removal of
Sand bed
8- 10 cm
Removal of
Schmutzdecke
%
Reductio
n
Schmu
tzdeck
e
Remov
al
0-2 cm
Sand
Bed
4-6 cm
Sand
Bed
8-10
cm
Sand
Bed
0.1 m/hr 31.02 25.14 24.90 28.94
0.5 m/hr 40.05 17.75 15.90 47.29
0.1m/hr
S
21.88 14.09 16.85 23.98
0.1m/hr
N
23.52 17.17 13.62 28.31

24. 24
Inactivation Potential
Removal of
Schmutzdecke
% Reduction in
columns
Schmutzdecke
Removal
0.1 m/hr 18.02
0.5 m/hr 40.05
0.1m/hr S 8.62
0.1m/hr N 8.92
1.5
1.23
1
0.6
1.74
1.59
1.87
1.69
0
0.4
0.8
1.2
1.6
2
80th day 91st day
DECMS2(LogRemoval)
Days
0.1 m/hr
0.5 m/hr
0.1 m/hr S
0.1 m/hr N

25. 25
To optimize
the Scarping
Procedure
Vertical
Biomass
Distribution
Spatial
Biomass
Distribution
Inactivation
potential
Biological
Activity

26. 26
Spatial Distribution of Biomass
0
20
40
60
80
100
120
140
160
180
200
220
schmutzdecke 0-2 cm 4-6 cm 8-10 cm
BiomassC(ng/g)
Sand Depth
KATWIJK
Location 1
Location 2
Location 3
0
20
40
60
80
100
120
140
160
180
200
220
schmutzdecke 0-2 cm 4-6 cm 8-10 cm
BiomassC(ng/g)
Sand Depth
MONSTER
Location 1
Location 2
Location 3

27. 27
To optimize
the Scarping
Procedure
Vertical
Biomass
Distribution
Spatial
Biomass
Distribution
Inactivation
potential
Biological
Activity

28. 28
Filter Schmutzdecke 0-2 cm 4-6 cm 8-10 cm
Monster 1.169±0.49 1.40±0.24 1.16±0.10 1.32±0.31
Katwijk 0.31±0.14 1.06±2.58 1.67±2.08 1.73±3.37
Columns
(0.1 m/hr)
0.85±0.19 0.78±0.37 0.66±0.02 0.78±0.13
Columns
(0.5 m/hr)
0.48±0.03 0.73±0.43 0.54±.03 1.45±0.04
Columns N*
(0.1 m/hr)
0.3±0.04 1.28±0.09 1.39±0.05 1.43±0.02
Columns S**
(0.1 m/hr)
2.09±0.44 1.48±0.22 1.57±0.59 1.44±.05
Biological Activity
Biological Activity was calculated by measuring ATP content /cell (10−7)

29. 29
Vertical
Biomass
Distribution
Spatial Biomass
Distribution
Inactivation
potential
Biological
Activity
To optimize
the Scarping
Procedure
~80 % of biomass accumulation
in the top 4 cm of sand bed.
Columns with added inoculum able to
mimic full scale filter.
.
Reduces after the removal of different layers of sand bed. More
than 1 log reduction after schmutzdecke removal.
Biological Activity is present
through out the filter bed
and higher in deeper layers
Uniform distribution of water
over sand bed leads to even
growth of biomass.

30. Results: Objective 2
30
Accelerate the
growth of
microorganism

31. 31
To accelerate
the biological
activity
Increasing the
filtration rate
Use of
Additional
nutrients
Use of
microbial
inoculum
Water
Quality
Monitoring
Chemical
Parameters
(DOC & TN)
Microbial
Parameters
(Bacteria and
Virus)
Physical
Parameters
(Turbidity &
Particle Counts)

32. 32
Physical Parameters
Columns Average Effluent
Turbidity (NTU)
(Standard
Deviation)
% Removal Median
value
(N=100)
0.1 m/hr 0.39±0.39 91.43 0.19
0.5 m/hr 0.62±0.54 86.20 0.34
0.1 m/hr
S*
0.59± 1.02 87.02 0.155
0.1 m/hr
N**
0.33±0.40 92.56 0.15
Columns Avg.
Effluent
Turbidity
(NTU)
%
Remov
al
Median
value
(N=100)
0.1 m/hr (39-
90)
0.12±0.0
39
98.09 0.10
0.5 m/hr (57-
90)
0.18±0.5
40
96.20 0.11
0.1 m/hr S*
(21-90)
0.13±
.049
97.98 0.10
0.1 m/hr N**
(18-90)
0.13±
.026
98.06 0.10
Turbidity
After
Ripening
* S= Added inoculum
**N= Added Nutrient Column with added inoculum and nutrients reached
faster to median turbidity levels below 0.1 NTU

33. 33
Column 0.1m/hr 0.5 m/hr 0.1m/hr S 0.1 m/hr N
Days of
operation
0-40 40-90 90-106 0-58 58-90 90-106 0-27 27-90 90-106 0-26 27-90 90-
106
Particle
Count/ml
605
±
200
138
±
60
454 ±
90
999
±
481
185 ±
58
1005 ±
231
875
±
581
133
±
37
112 ±
4
606
±
234
130 ±
30
118
± 7
% Removal 93.9
±
2.1
98. 6
±
0.9
95.4
±
0.91
89.8
±
4.5
98.1±
0.6
89.92 ±
2.3
91.22
±
5.8
98.6 ±
0.4
98.8 ±
0.1
93.9
±
2.4
98.69
±
0.3
99.8 ±
0.1
Particle Counts
Physical Parameters
Column with added inoculum and nutrients reached faster to
particle counts below 200/ml

34. 34
Chemical Parameters
0
5
10
15
20
25
30
35
40
1 8 15 22 29 36 43 50 57 64 71 78 85
%DOCRemoval
Days
0.1 m/hr
0.5 m/hr
0.1 m/hr S
0.1 m/hr N
0
4
8
12
16
20
1 8 15 22 29 36 43 50 57 64 71 78 85
%TNRemoval
Days
0.1 m/hr
0.5 m/hr
0.1 m/hr S
0.1 m/hr N
Average DOC and TN removal ranged from 16.23 to 28.82 % and 6.82 to 12.53 % respectively

35. 35
Days 0.1 m/hr 0.5 m/hr 0.1 m/hr S* 0.1 m/hr
N**
Spike 1
(DEC)
E. Coli 30 1.27 1.84 2.84 1.31
MS2 0.56 0.41 0.76 0.69
Spike 2
(DEC)
E. Coli 44 3.42 2.80 3.95 3.50
Spike 3
(DEC)
E. Coli 58 4.45 3.17 5.20 4.71
Spike 4
(DEC)
E. Coli 80 4.90 3.57 5.30 5.10
MS2 1.50 0.89 1.74 1.99
Microbial parameters were measured by carrying out spiking experiments
Microbial parameters
* S= Added inoculum
**N= Added Nutrient

36. 36
To accelerate
the biological
activity
Increasing the
filtration rate
Use of
Additional
nutrients
Use of
microbial
inoculum
Water
Quality
Monitoring
Chemical
Parameters
(DOC & TN)
Microbial
Parameters
(Bacteria and
Virus)
Physical
Parameters
(Turbidity &
Particle Counts)
Columns with added inoculum were able to perform better than
other columns in terms of turbidity, particle counts removal and
DEC of bacteria.

37. Results: Objective 3
37
Indicators of
Ripening

38. 38
• Correlation analysis was performed between
indicators.
• Reductions of bacteria, viruses, turbidity and
particle counts increase substantially with time
as filter ripens.
• No such pattern was observed in DOC and TN
removal
Water
Quality
Monitoring
Chemical
Parameters
(DOC & TN)
Microbial
Parameters
(Bacteria and
Virus)
Physical
Parameters
(Turbidity &
Particle Counts)

39. 39
Correlation between Turbidity & Particle Count
• R² between particle count &
E.Coli 0.72 as compared to
0.68 between Turbidity &
E.Coli .
• Particle Count a better
surrogate than turbidity
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
0
200
400
600
800
1000
0 15 30 45 60 75 90 105
Turbidity(NTU)
ParticleCounts/ml
Days
Particle Count
Turbidity
Removal of
Schmutzdecke
0
0.5
1
1.5
2
2.5
0
500
1000
1500
2000
0 15 30 45 60 75 90 105
Turbidity(NTU)
ParticleCounts/ml
Days
Particle Count
Turbidity
Removal of
Schmutzdecke
(a)0.1m/hr
(a)0.5m/hr
• Correlation Analysis was
performed between
turbidity and particle
counts.

40. 40
Knowledge
Gaps
Objective
and
Approach
Materials &
Methods
Results Conclusion

41. Conclusion
To Optimise the
scraping procedure:
only top 4 cm of sand
bed including
schmutzdecke needs
to be scraped.
1 2 3
41

42. Conclusion
To Optimise the
scraping procedure :
only top 4 cm of sand
bed including
schmutzdecke to be
cleaned
1
Addition of microbial
inoculum presents the
best alternative to reduce
the startup time of slow
sand filters.
2 3
42

43. Conclusion
43
To Optimise the
scraping procedure
only top 4 cm of sand
including
schmutzdecke to be
cleaned.
1
Addition of microbial
inoculum presents the
best alternative to reduce
the startup time of slow
sand filter
2
Particle Count &
Turbidity should be
together used as
indicators of ripening
3

44. Conclusion
To Optimise the
scraping procedure:
only top 4 cm of sand
bed including
schmutzdecke needs
to be scraped
1
Addition of microbial
inoculum presents the
best alternative to reduce
the startup time of slow
sand filters.
2
Particle Count &
Turbidity should be
together used as
indicators of ripening
of filter
3
44

45. Recommendation
45

46. Recommendations
Further
research work
on
pilot scale
system
Source of
Nutrients
Concertation of
Nutrients
Concentration of
Inoculum
Work must be
carried out in Cold
temperature
Use of
positively
charged media
on surface bed
46

47. 47
Thank You !!!