A Mesocosm Study of Floating Islands Environmental Solution
“BioHaven” for Nutrient Removal in Stormwater Ponds
Final Report
Submitted to
Martin Ecosystems, Inc.
(Formerly Floating Island International Inc.)
Submitted by
PIs: Dr. Ni-Bin Chang and Dr. Martin Wanielista
Team Members: Kamrul Islam, Zachary Marimon, Zhemin Xuan
Lee Mullon, Benjamin Vannah
Stormwater Management Academy
Department of Civil, Environmental, and Construction Engineering
University of Central Florida, Orlando, Florida, 32816, USA
EXECUTIVE SUMMARY
In Florida, stormwater retention and detention ponds are widely used to provide water
quality treatment as well as flood and downstream erosion control. Wet pond (also called wet
detention pond) retains water all the time. On the other hand, detention pond (also called dry
pond, dry detention basin) temporarily detains the water and will drain shortly after the storm
events. However, excess nutrients, such as nitrogen and phosphorus, flow into the wet detention
ponds with surface runoff and lead to environmental concerns such as surface water
eutrophication and groundwater/springwater contamination. The resulting overgrowth of algae or
invasive plants can adversely affect ecosystems. Further, the State of Florida has recently
adopted numeric criteria for total nitrogen and total phosphorus in Chapter 62-302, F.A.C. Thus,
the removal of nutrients from stormwater has been considered a priority for urban water quality
management. Thus, floating island technology has received attention in the stormwater
management community as an eco-friendly alternative for providing additional nutrient
removals.
In this study, BioHaven Floating Islands® were tested through both mesocosms and a
real-world wet pond to test their potential use as a Best Management Practice (BMP) to be
incorporated in stormwater wet ponds as Floating Treatment Wetlands (FTWs). The operation
period of the mesocosm study was designed to focus on observations of macrophyte–epiphyte–
phytoplankton interactions in order to understand temporal characteristics of ecological
phenomena. Water quality parameters of concern included total nitrogen (TN), nitrite-nitrogen
(NO2-N), nitrate-nitrogen (NO3-N), ammonia-nitrogen (NH3-N), total phosphorus (TP) and
orthophosphate (OP) in addition to in-situ parameters such as pH, dissolved oxygen,
temperature, and chlorophyll-a (Chl-a). Percent area coverage of the vegetation, floating
macrophytes, and littoral zone emergent plants were varied in grouped mesocosms to determine
I the best combination for optimal nutrient removal efficiency, which would be further
implemented in the actual wet pond located in a neighboring community close to the main
campus of University of Central Florida. After field deployment of FTWs, the pond was closely
monitored on a regular basis to understand the hydrological cycle and average nutrient load
reduction. For non-storm events, phosphorus removal was substantial; about 46.3 % TP and
79.5% OP were removed. The removal of TN, NO2-N + NO3-N, and NH3-N also achieved 16.9,
16.7, and 53.0 %, respectively. The removal was calculated on EMCs assumed for multi-family
land uses. The removal efficiency of before and after deployment was compared to assess the
additional credit attributed to the BioHaven floating wetlands. The term operating hydraulic
residence time (HRT) was defined to demonstrate floating treatment wetland performance in the
field pond; a longer HRT generally leads to higher TP and TN removal efficiencies. Based on the
approach for evaluating the performance credit of FTWs, additional 2.41 – 6.06 % of TN and
1.23 – 2.37 % of TP (in terms of marginal concentration improvement) and 24.6 % of TN and
29.1 % of TP were removed by BioHaven FTWs according to an assumed land use EMC value
based and inlet value based credit, respectively. The pond also had a water fountain that is
assumed to contribute nutrients resuspension from sediment to the water column.
II TABLE OF CONTENTS
EXECUTIVE SUMMARY ....................................................................................... I
TABLE OF CONTENTS ..........................................................................................1
LIST OF FIGURES ...................................................................................................2
LIST OF TABLES ....................................................................................................3
CHAPTER 1: INTRODUCTION AND STUDY GOALS .......................................4
1.1 BACKGROUND ..............................................................................................4
1.2 OBJECTIVES ..................................................................................................7
CHAPTER 2: MESOCOSM STUDY .......................................................................9
2.1 SELECTION OF PLANT SPEICES................................................................9
2.2 EXPERIMENTAL DESIGN..........................................................................10
2.3 EXPERIMENTAL SETTINGS .....................................................................11
2.4 SAMPLING AND MEASUREMENTS ........................................................12
2.5 RESULTS AND DISCUSSION ....................................................................14
CHAPTER 3: FIELD STUDY ................................................................................22
3.1 EXPERIMENTAL DESIGN ..........................................................................22
3.2 EXPERIMENTAL SETTINGS .....................................................................30
3.3 SAMPLING AND MEASUREMENTS ........................................................31
3.4 RESULTS AND DISCUSSION ....................................................................32
CHAPTER 4 CONCLUSIONS ...............................................................................42
REFERENCES ........................................................................................................44 1 LIST OF FIGURES
Figure 1: Phytoplankton bloom in studied pond (Photo taken 4/29/2011). .................................... 5
Figure 2: Cross Section of a BioHaven Floating Island. ................................................................ 6
Figure 3: Selected floating and emergent macrophytes (photo courtesy of Beeman’s Nursery). 10
Figure 4: Schematic diagram of the mesocosm setup. .................................................................. 11
Figure 5: Experiment setup ........................................................................................................... 12
Figure 6: Average bi-weekly nutrient removal efficiencies. ........................................................ 21
Figure 7: The location of actual pond, Pond 5, in the community................................................ 22
Figure 8: Water level sensor ......................................................................................................... 23
Figure 9: Rain gauge. .................................................................................................................... 23
Figure 10: Evaporation pan. .......................................................................................................... 25
Figure 11: Deployment of floating wetland (photos taken 7/15/2011)......................................... 30
Figure 12: Sampling locations at the Pond 5 (Google Earth, taken 5/1/2011). ............................ 31
Figure 13: Nutrients concentration during pre-analysis................................................................ 34
Figure 14: Nutrients concentration during post-analysis. ............................................................. 37
Figure 15: Operating HRT vs. TN removal in a pond with a water fountain and FTWs ............. 40
Figure 16: Operating HRT vs. TP removal in a pond with a water fountain and FTWs. ............. 40
2 LIST OF TABLES
Table 1: Component of the mesocosms. ....................................................................................... 10
Table 2: Chemical analysis methods............................................................................................. 13
Table 3: Bi-weekly total phosphorus concentrations (in mg.L−1)................................................. 14
Table 4: Bi-weekly orthophosphate concentrations (in mg.L−1). ................................................. 15
Table 5: Bi-weekly total nitrogen concentrations (in mg.L−1). ..................................................... 15
Table 6: Bi-weekly nitrate-nitrogen concentrations (in mg·L−1). ................................................. 16
Table 7: Bi-weekly Ammonia-nitrogen concentrations (in mg.L−1) ............................................ 16
Table 8: pH values over the observation period. .......................................................................... 17
Table 9: Electrical conductivity (in µS.cm−1) over the observation period. ................................. 17
Table 10: Temperature (in °C) over the observation period. ........................................................ 17
Table 11: Dissolved oxygen (in mg.L−1) over the observation period. ........................................ 18
Table 12: Turbidity (in NTU) over the observation period. ......................................................... 18
Table 13: Chlorophyll-a (in µg.L−1) over the observation period................................................. 18
Table 14: GroupWise evolution and proportion of epiphytes, phytoplankton, and other fauna. . 20
Table 15: Watershed area and runoff coefficient used for Pond 5. .............................................. 24
Table 16: Outline of analytical methods. ...................................................................................... 31
Table 17: Nutrients concentration for storm events during pre-analysis (mg.L−1). ...................... 33
Table 18: Nutrients concentration for non-storm events during pre-analysis (mg.L−1). .............. 33
Table 19: Nutrients concentration for storm events during post-analysis (mg.L−1). .................... 36
Table 20: Nutrients concentration for non-storm events during post-analysis (mg.L−1). ............. 36
Table 21: Operating HRT associated with TN removal efficiencies. ........................................... 38
Table 22: Operating HRT associated with TP removal efficiencies............................................. 39
Table 23: Credit of FTWs in Pond 5 with a Water Fountain ........................................................ 41
3 CHAPTER 1: INTRODUCTION AND STUDY GOALS
1.1 BACKGROUND
Today, nutrients such as ammonia, nitrite, nitrate, and phosphorus in stormwater effluents
are common contaminants in water bodies that affect public health and ecosystem integrity with
acute and chronic harmful outcomes. For example, without proper treatment, ammonia in
wastewater effluents can stimulate phytoplankton growth, exhibit toxicity to aquatic biota, and
exert an oxygen demand in surface waters (Beutel, 2006). Undissociated ammonia is extremely
volatile and either ionizes or volatizes in aqueous solution. Ionized ammonia is very toxic for
fish species (Tarazona et al., 2008). Fish mortality, health, and reproduction can be affected by
the presence of a minute amount of ammonia-nitrogen (NH3-N) (Servizi and Gordon, 2005).
Nitrate can cause human health problems such as liver damage and even cancers (Gabel et al,
1982; Huang et al., 1998), as well as bind with hemoglobin to cause methemoglobinemia, an
oxygen deficiency in infants (Kim-Shapiro et al., 2005). Nitrite can react with amines chemically
or enzymatically to form nitrosamines that are very potent carcinogens (Sawyer et al., 2003).
In Florida, stormwater ponds are commonly built in ordinary residential areas, providing
aesthetic benefits as well as flood and downstream erosion control. However, due to increased
human activity, fertilizers, animal excrement, and organic debris enter the ponds with stormwater
runoff. Excessive nutrients that ponds cannot handle naturally result in new environmental
issues, such as eutrophication. As a result of this dangerous cycle, algal and phytoplankton
blooms will gradually fill the entire water body and prevent sunlight from penetrating the water
column (Figure 1), which hinders oxygen transfer and inhibits a healthy aquatic ecosystem.
4 Figure 1: Phytoplankton bloom in studied pond (Photo taken 4/29/2011).
Constructed wetlands use various aquatic plants to remediate nutrient-rich surface and
subsurface flow in both stormwater and wastewater (Iamchaturapatra et al., 2007), and their
application has significantly increased (White et al., 2009; Baldwin et al., 2009; Belmont and
Metcalfe, 2003). Macrophytes in floating treatment wetlands (FTWs) remove pollutants by
directly assimilating them into plant tissue, providing a suitable environment for microorganisms
to transform pollutants and reduce their concentrations (Breen, 1990; Billore and Sharma, 1996),
a process that qualifies as a potential best management practice (BMP).
Stormwater runoff is highly variable due to the erratic nature of storm events in both
intensity and duration; thus, sediment-rooted plants in conventional treatment wetlands
experience a range of water depths and periods of inundation (Greenway and Polson, 2007). The
duration of inundation, water depth, and flood or drought frequency affect plant growth,
establishment, and survival. Long periods of flooding are stressful to some bottom-rooted
wetland plants (Ewing, 1996; Headley et al., 2006). To manage this issue, the wetland area might
5 be increased to buffer against extremes during water level fluctuations, or high flows can be
bypassed. In the case of bypass, however, a significant portion of incoming stormwater will not
be treated (Headley et al., 2006), and the large land area required for installation is a definite
limitation. FTWs are an innovative variant on these systems and a possible solution to this
problem. Plants grow on floating mats rather than rooted in the sediments (Figure 2); therefore,
water depth is not a concern, and the mats are likely unaffected by fluctuations in water levels.
Figure 2: Cross Section of a BioHaven Floating Island.
Biologically, aquatic macrophyte-based wastewater treatment systems are far more
diverse than present-day mechanical treatment systems (Hammer, 1989; Moshiri, 1993). Freefloating macrophytes shade the water column, resulting in a cooler habitat for fish and
macroinvertebrates (Nahlik and Mitsch, 2006). The hanging roots provide a large surface area
for denitrifying bacteria, creating an anaerobic environment that can remove nitrate by the
denitrification process (Govindarajan, 2008), and entrapping fine suspended particulates that
would otherwise remain in suspension in a conventional pond system (Headley and Tanner,
2006). Microbes that live on the surface of plant roots in a wetland remove 10 times more nitrate
6 than do the plants themselves (Adams, 1992). These microbes change nitrate-nitrogen (NO3-N)
to NH4-N in a process called dissimilatory nitrate reduction (DNRA). Similarly, growth of
microbes, protozoa, and algae (zooglea), forms a thin biofilm on all wetted surfaces, including
roots, rhizomes and root hairs. This biofilm incorporates nutrients and metals into its biomass,
enhancing removal rates (Kadlec, 2009).
Because plants in floating wetlands are not rooted in sediments, they are forced to acquire
nutrition directly from the water column (Headley et al., 2006; Vymazal, 2007). Nutrient and
other element uptake into biomass rate increases as physiological growth continues. Total
nitrogen (TN) and total phosphorus (TP) can be further removed if the plants are harvested after
peak biomass is obtained. However, any harvesting of biomass/biofilm should be performed in
stages, leaving some plants in various stages of growth (seedling, mid-growth, and approaching
maturity). Otherwise, a complete loss of the benefit of the FTW would be experienced during a
whole-system restart while the plants and other biofilm become established. The benefits of
harvesting FTWs warrant further study. Finally, by reducing available nutrient levels, algal toxin
growth can be reduced or avoided.
1.2 OBJECTIVES
The objectives of this study were to (1) explore the engineering design startegies of
FTWs and (2) conduct research to determine the waste load reduction efficiencies of nutrients in
a wet pond. We hypothesized that (1) area coverage of floating mats would have a significant
impact on nutrient removal efficiency; (2) existence of a littoral zone would improve the water
quality in terms of reducing turbidity, Chl-a, and other components, and might change the
nutrient removal efficiencies by acting either as a sink for pollutants or removing them; and (3)
7 FTWs would be an alternate solution for common stormwater detention pond problems by
suppressing unwanted species such as algae and duckweed. The effect of percent area coverage
and the littoral zone were evaluated through regular monitoring of water quality parameters.
Temporal observation and unwanted plant species identification helped elucidate ecological
evolution and interactions and provided the knowledge basis for actual pond application.
8 CHAPTER 2: MESOCOSM STUDY
2.1 SELECTION OF PLANT SPEICES
Various species were found to be suitable for FTWs. Pioneer floating mat-forming
species include Typha latifolia, T. angustifolia, Phragmites australis, Panicum hemitomon,
Glyceria maxima, Carex lasiocarpa, Menyanthes trifoliate, Myrica gale, and Chamaedaphne
calyculata (Headley et al. 2006). Water hyacinths (Eicchornea crassipes) and duckweed species
(Lemna, Spirodela, and Wolfiella) were also regarded as typical plant species for use in largescale application in floating wetlands (Kadlec et al. 1996; DeBusk et al. 1995). These were
candidate plants along with others being used by local nurseries in their promotion of floating
islands. T. japonica, E. crassipes, and Pristia stratiotes had high nutrient removal efficiencies
when rates were calculated by biomass-based method, but low efficiencies when calculated by
area-based method (White et al. 2009). Both Juncus effussus and pickerelweed (Pontederia
cordata) are indigenous to the wetlands of south-eastern United States and have proven to be
effective at taking up nutrients. Another species, Agrostis alba, is also effective but not native in
Florida. Considering all these, Juncus (Figure 3a) and pickerelweed (Figure 3b) were selected for
the mesocosm study, and a mixture of bulrush (Scirpus californicus) (Figure 3c) and
pickerelweed were selected as the emergent macrophytes for littoral zones because they are
endemic flora of Florida.
9 (a) Juncus
(b) Pickerelweed
(c) Bulrush
Figure 3: Selected floating and emergent macrophytes (photo courtesy of Beeman’s
Nursery).
2.2 EXPERIMENTAL DESIGN
Ten scenarios were created varying percent area coverage, littoral zone, and plant species
(Figure 4 and Table 1). Case-1 and Case-2 had no floating macrophytes and served as control
cases. Considering feasibility in an actual pond, percent area coverage was limited to 10%. A
slope of 1:5 was maintained toward the center of the cylindrical mesocosms for the bottom
sediment layer.
Table 1: Component of the mesocosms.
Scenario
Case-1*
Case-2*
Case-3
Case-4
Case-5
Case-6
Case-7
Case-8
Case-9
Case-10
Littoral Zone
Area
Coverage
0%
10%
10%
10%
5%
5%
10%
10%
5%
5%
No
No
Yes
Yes
Yes
Yes
No
No
No
No
* Control Case
10 Plant Species
N/A
N/A
Juncus
Pickerelweed
Juncus
Pickerelweed
Juncus
Pickerelweed
Juncus
Pickerelweed
Figure 4: Schematic diagram of the mesocosm setup.
2.3 EXPERIMENTAL SETTINGS
Mesocosms were composed of two sizes of cylindrical plastic tanks, 5 × 1.2 m and 3 ×
0.8 m with water holding capacities of 18,000 and 4,000 L, respectively. Bottom soil was
collected from an actual pond and placed under all mesocosms (Figure 5a) for planting emergent
littoral zone plants (Figure 5c). Sediment was also placed under mesocoms with no littoral zone
to mimic an actual pond environment. For proper light, wind, and seasonal variation, mesocosms
were placed in an open field (Figure 5e) to mimic actual pond conditions of aeration due to wind,
rainfall events, and evaporation.
11 FTW treatments consisted of fibrous polyester mats (Figure 5d) injected with expanded
polyurethane to provide buoyancy (BioHavenTM Floating Islands, Floating Island International).
The center of the mats was filled with a growth medium (8 cm deep) consisting of sand, peat,
and compost (1:2:1); 100% Canadian peat was used around the root zone as sorption media.
(a) (d) (b) (e) (c) Figure 5: Experiment setup: (a) placement of bottom sediment, (b) mesocosms with
stormwater, (c) plantings in the littoral zone, (d) floating mat, and (e) all mesocosms after
setup.
2.4 SAMPLING AND MEASUREMENTS
According to The National Stormwater Quality Database (Pitt et al., 2004), stormwater
runoff contains on average 3 mg.L−1 TN and ˂1 mg.L−1 TP; therefore, nutrients were dosed to
determine nutrient removal efficiency. Commonly used fertilizers potassium nitrate (KNO3) and
monopotassium phosphate (KH2PO4) were used in this case. Dosing and addition of new
12 stormwater were performed once every 30 days to imitate periodic nutrient-rich surface runoff.
Samples were collected on a biweekly basis over 3 months. Samples collected from five different
points were mixed to form a composite sample deemed representative of the entire mesocosm.
A DR 2800 Spectrophotometer was used to analyze nutrient concentrations. A variety of
methods were used in chemical analyses (Table 2). To maintain Quality Assurance/Quality
Control (QA/QC) protocol, duplicate samples were analyzed every 10 samples. Samples were
preserved with acidification when necessary, and percent recovery was ensured within 80–120%
each time. All water sampling equipment was acid-rinsed, followed by flushing in distilled water
prior to sampling of each tank.
Table 2: Chemical analysis methods.
Parameter
Method
pH
Hach HQ40d
Conductivity
Hach HQ40d
Dissolved Oxygen
Hach HQ40d
Turbidity
Turbidimeter
Chl-a
Aquafluor™ Handheld Fluorometer
Total Nitrogen
Persulfate digestion method (Hach Method 10071)
Ammonia- Nitrogen
Salicylate Method (Hach Method 8155)
Nitrate- Nitrogen
Cadmium reduction method (Hach Method 8192, 8171)
Total Phosphorus
Acid persulfate digestion method (Hach Method 8190)
Orthophosphate
PhosVer 3 (Ascorbic Acid) Method (Hach Method 8048)
13 2.5 RESULTS AND DISCUSSION
Due to differences in bottom mud compaction and corresponding changes in water
volume, it was difficult to maintain constant initial nutrient loading in our experiment; therefore,
a small deviation from the usual stormwater quality was observed in the initial nutrient
concentrations. Both influent (0 Day) and effluent (15 and 30 Days) concentrations of various
nutrients (Table 3–7) indicate the efficacy of the BioHaven FTW system. More water quality
constitutes of concern are listed in Table 8-13.
Table 3: Bi-weekly total phosphorus concentrations (in mg.L−1).
Scenario
0*
Day
Case-1
Case-2
Case-3
Case-4
Case-5
Case-6
Case-7
Case-8
Case-9
Case-10
3.476
3.506
2.058
2.053
1.826
3.063
3.383
2.737
3.191
3.659
Month-1 15
30
Days
Days
2.659
1.205
0.506
1.949
0.624
2.013
1.723
1.531
0.979
0.891
1.156
0.673
0.265
0.821
0.442
0.932
1.122
0.713
0.742
0.595
0*
Day
Month-2 15
30
Days
Days
2.460
1.980
1.648
2.188
1.562
3.194
2.166
1.481
1.190
2.029
1.921
1.122
0.987
1.562
0.871
2.591
1.349
0.781
0.882
1.031
* Nutrients were dosed in liquid form
14 0.719
0.661
0.694
0.983
0.394
1.348
0.719
0.305
0.290
0.482
0*
Day
2.664
1.333
0.801
2.097
2.220
0.462
1.289
1.181
1.161
0.806
Month-3 15
30
Days
Days
0.698
0.673
0.383
1.457
0.321
0.417
0.737
0.489
0.737
0.353
0.329
0.417
0.358
0.393
0.000
0.092
0.432
0.220
0.240
0.191
Table 4: Bi-weekly orthophosphate concentrations (in mg.L−1).
Scenario
0
Day
Case-1
Case-2
Case-3
Case-4
Case-5
Case-6
Case-7
Case-8
Case-9
Case-10
1.380
1.838
1.105
1.777
1.414
2.079
1.963
1.824
1.523
1.682
Month-1 15
30
Days
Days
1.073
0.551
0.227
0.927
0.392
1.337
0.938
0.642
0.386
0.390
0.504
0.263
0.156
0.648
0.281
0.806
0.752
0.469
0.253
0.319
0
Day
Month-2 15
30
Days
Days
1.783
1.652
1.229
1.898
1.115
2.569
1.887
0.992
0.722
1.864
1.231
0.783
0.674
1.149
0.657
1.980
0.768
0.439
0.561
0.720
0.411
0.328
0.451
0.542
0.118
0.882
0.651
0.102
0.023
0.182
0
Day
1.010
0.792
0.593
0.843
0.589
0.394
0.970
0.874
0.559
0.589
Month-3 15
30
Days
Days
0.422
0.242
0.367
0.811
0.304
0.299
0.162
0.462
0.075
0.227
0.274
0.128
0.043
0.172
0.000
0.000
0.135
0.130
0.000
0.067
Table 5: Bi-weekly total nitrogen concentrations (in mg.L−1).
Scenario
0
Day
Case-1
Case-2
Case-3
Case-4
Case-5
Case-6
Case-7
Case-8
Case-9
Case-10
4.783
3.078
3.862
3.954
3.677
3.263
3.124
3.908
3.309
3.862
Month-1 15
30
Days
Days
3.032
2.433
2.341
2.111
2.111
2.249
2.203
2.295
2.618
2.341
2.664
2.341
2.018
1.972
1.972
2.065
2.203
2.249
2.018
2.065
0
Day
Month-2 15
30
Days
Days
4.032
3.277
2.202
3.129
3.387
2.251
4.057
3.528
3.220
3.115
2.078
1.739
1.938
2.131
2.271
2.025
2.010
1.773
1.460
2.090
15 1.693
0.966
0.849
1.513
1.345
1.554
0.882
0.816
0.973
1.082
0
Day
4.599
3.862
3.631
5.244
3.355
4.046
3.954
3.539
4.230
4.829
Month-3 15
30
Days
Days
4.184
3.585
2.802
3.816
3.171
3.217
3.447
3.401
3.124
2.618
3.954
3.078
2.387
3.585
2.249
2.479
2.479
3.032
2.387
2.387
Table 6: Bi-weekly nitrate-nitrogen concentrations (in mg·L−1).
Scenario
0
Day
Case-1
Case-2
Case-3
Case-4
Case-5
Case-6
Case-7
Case-8
Case-9
Case-10
1.032
1.106
1.488
1.718
1.028
0.984
1.732
1.233
1.900
1.847
Month-1 15
30
Days
Days
0.193
0.055
0.098
0.075
0.052
0.036
0.068
0.239
0.087
0.202
0.236
0.002
0.032
0.018
0.006
0.036
0.041
0.064
0.004
0.038
0
Day
Month-2 15
30
Days
Days
1.341
0.976
1.105
0.793
1.169
1.040
1.014
1.014
1.407
1.418
0.114
0.024
0.037
0.064
0.267
0.046
0.024
0.036
0.023
0.239
0.029
0.011
0.028
0.034
0.089
0.031
0.019
0.027
0.016
0.100
0
Day
0.575
0.975
0.731
0.453
0.453
0.487
0.575
1.021
0.623
0.855
Month-3 15
30
Days
Days
0.068
0.000
0.020
0.022
0.013
0.004
0.025
0.142
0.002
0.015
0.034
0.006
0.061
0.052
0.000
0.018
0.043
0.050
0.000
0.011
Table 7: Bi-weekly Ammonia-nitrogen concentrations (in mg.L−1)
Scenario
0
Day
Case-1
Case-2
Case-3
Case-4
Case-5
Case-6
Case-7
Case-8
Case-9
Case-10
0.216
0.081
0.141
0.051
0.075
0.079
0.085
0.148
0.134
0.107
Month-1 15
30
Days
Days
0.147
0.090
0.086
0.099
0.093
0.084
0.097
0.161
0.085
0.082
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0
Day
Month-2 15
30
Days
Days
0.127
0.070
0.187
0.086
0.157
0.107
0.114
0.129
0.068
0.096
0.023
0.017
0.031
0.042
0.030
0.017
0.084
0.013
0.068
0.055
16 0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0
Day
0.066
0.101
0.088
0.126
0.114
0.105
0.061
0.104
0.074
0.130
Month-3 15
30
Days
Days
0.065
0.079
0.082
0.090
0.016
0.050
0.072
0.038
0.039
0.069
0.029
0.037
0.030
0.052
0.037
0.034
0.047
0.024
0.009
0.040
Table 8: pH values over the observation period.
Scenario
0
Day
15
Days
30
Days
45
Days
60
Days
75
Days
90
Days
Case-1
Case-2
Case-3
Case-4
Case-5
Case-6
Case-7
Case-8
Case-9
Case-10
7.36
7.48
7.45
7.51
7.42
7.45
7.66
7.60
7.34
7.52
7.80
8.95
8.03
8.02
7.76
8.52
8.50
8.20
7.76
8.17
8.01
8.81
8.05
8.09
8.04
8.08
8.35
7.90
8.00
8.28
7.98
8.60
8.20
8.08
8.09
8.34
8.26
7.54
7.80
8.29
8.00
8.45
7.85
7.64
7.88
8.78
8.03
8.13
8.31
8.57
7.50
7.99
8.02
7.33
8.10
8.22
8.11
8.47
8.01
8.90
7.71
8.30
8.04
7.53
8.03
8.95
8.09
8.12
8.06
8.85
Table 9: Electrical conductivity (in µS.cm−1) over the observation period.
Scenario
0
Day
15
Days
30
Days
45
Days
60
Days
75
Days
90
Days
Case-1
Case-2
Case-3
Case-4
Case-5
Case-6
Case-7
Case-8
Case-9
Case-10
129.1
156.0
194.1
152.8
153.2
202.5
153.5
218.0
157.2
141.8
150.7
159.8
208.7
152.2
143.5
191.3
152.6
217.6
160.9
148.0
169.1
177.5
229.0
160.1
135.4
209.9
149.6
228.0
165.4
170.1
170.5
166.4
232.3
147.8
113.6
187.5
165.4
210.5
160.3
188.2
200.6
206.6
237.0
170.6
147.5
227.0
180.8
253.0
182.1
197.2
167.3
162.8
204.9
129.7
118.1
190.2
153.8
215.4
115.3
165.8
145.9
161.5
194.4
121.7
103.1
171.9
155.1
201.3
143.2
159.3
Table 10: Temperature (in °C) over the observation period.
Scenario
0
Day
15
Days
30
Days
45
Days
60
Days
75
Days
90
Days
Case-1
Case-2
Case-3
Case-4
Case-5
Case-6
Case-7
Case-8
Case-9
Case-10
29.5
28.5
28.3
28.4
29.3
26.8
28.7
28.7
29.4
29.0
26.2
25.9
24.8
26.2
26.1
26.9
27.1
25.7
25.8
27.1
25.7
26.1
25.1
25.6
26.1
26.2
27.7
25.2
25.4
26.7
30.1
30.0
29.8
29.9
30.0
30.2
30.2
30.5
29.7
30.8
29.9
30.2
30.0
29.8
30.1
30.3
30.1
30.0
30.4
31.0
29.0
29.8
29.0
29.3
28.4
28.7
30.4
29.9
28.7
30.3
30.8
29.9
30.2
31.7
30.1
29.8
30.0
29.3
30.3
30.4
17 Table 11: Dissolved oxygen (in mg.L−1) over the observation period.
Scenario
0
Day
15
Days
30
Days
45
Days
60
Days
75
Days
90
Days
Case-1
Case-2
Case-3
Case-4
Case-5
Case-6
Case-7
Case-8
Case-9
Case-10
6.72
5.43
3.48
7.76
5.60
6.02
5.87
5.83
5.45
7.73
5.81
8.70
6.28
7.88
7.08
8.27
7.12
5.70
5.57
4.93
6.22
8.39
5.52
7.82
6.77
7.03
9.23
5.16
7.86
6.87
8.32
7.35
4.09
3.28
6.61
7.29
10.16
5.99
7.70
6.90
5.18
8.06
4.47
2.78
6.14
7.24
11.60
6.23
6.57
8.68
9.49
9.70
6.36
7.86
8.16
6.29
5.15
7.14
5.84
9.36
8.43
6.10
5.82
7.89
9.01
Out of Range
7.01
2.01
4.91
6.37
Table 12: Turbidity (in NTU) over the observation period.
Scenario
0
Day
15
Days
30
Days
45
Days
60
Days
75
Days
90
Days
Case-1
Case-2
Case-3
Case-4
Case-5
Case-6
Case-7
Case-8
Case-9
Case-10
28.00
3.00
93.00
15.00
2.00
4.00
5.00
6.00
7.00
31.00
39.00
7.00
21.00
4.00
5.00
3.00
6.00
6.00
4.00
4.00
34.00
5.00
12.00
4.00
6.00
5.00
5.00
3.00
2.00
4.00
22.56
7.41
11.20
5.51
4.88
4.25
5.79
11.61
2.19
3.78
17.60
8.33
10.20
6.36
3.99
1.63
5.29
27.10
2.21
3.85
14.70
7.35
9.83
4.79
5.23
2.99
6.35
11.60
8.78
2.72
5.10
3.35
8.28
5.49
1.44
2.16
5.00
5.56
6.96
3.46
Table 13: Chlorophyll-a (in µg.L−1) over the observation period.
Scenario
0
Day
15
Days
30
Days
45
Days
60
Days
75
Days
90
Days
Case-1
Case-2
Case-3
Case-4
Case-5
Case-6
Case-7
Case-8
Case-9
Case-10
4.46
0.92
2.03
1.81
1.28
1.43
1.53
2.02
1.12
1.86
4.36
1.30
2.01
1.46
1.48
1.23
1.57
1.61
1.37
1.06
2.26
0.95
2.74
1.81
1.32
1.51
2.04
1.67
1.47
1.14
3.19
0.81
1.82
3.77
2.01
1.49
4.67
2.01
1.39
1.92
4.38
1.42
1.78
5.02
2.39
1.56
4.36
2.06
1.20
1.30
1.65
1.32
1.76
1.81
2.03
1.82
4.72
1.87
2.66
0.93
2.23
1.49
1.66
5.77
1.58
1.63
2.47
1.67
2.89
2.42
18 Although the control case (Case-1) was expected to show little nutrient removal, growth
of undesirable plant species like duckweed (Lemna minor) and algae hampered our comparison.
In other cases, effluent concentrations were satisfactorily low. The absence of macrophyte
plantings in the control case allowed duckweed to grow and cover the surface, resulting in a
significant amount of nutrient removal. Duckweed requires a lot of nutrients to grow, so typically
they are found in nutrient-rich environments. A surface layer of duckweeds prevents sunlight
from reaching the deeper parts of the water column so that underwater plants and algae can no
longer photosynthesize and produce oxygen, which can greatly stress or even kill fishes. Most ecological findings were reported in a sequential manner (Table 14). After 1 month,
the control case (Case-1) became infested (40%) with duckweed due to the absence of
macrophytes. Other mesocosms also had partial duckweed coverage; although they had floating
macrophytes or littoral zone, they somehow had redundant nutrients for duckweed.
Algae and duckweed are natural competitors. As soon as duckweed was removed from
the mesocosms, algal growth was noticed (after 2 months), mostly filamentous blue-green algae
(Cyanophyceae). Laboratory tests identified that the majority of samples contained Oscillatoria
as well as some Microcystis and Ankistrodemus. After 3 months, an increase in the proportion of
epiphytes and phytoplankton was noted, and the existence of fishes and frogs was observed over
time.
From the above observation over these temporal ecological changes, it is evident that
FTWs can significantly suppress algae and duckweed growth, especially when compared with
the control cases. A few mesocosms showed a significant amount of duckweeds or algae despite
the presence of sufficient macrophytes, which might be why littoral zone plants were not merely
19 an inert substratum for algal attachment, but rather served as a nutrient source that significantly
influenced epiphyte P metabolism throughout the growing season. Bottom sediments might also
periodically release extra nutrient as they were saturated.
Table 14: GroupWise evolution and proportion of epiphytes, phytoplankton, and other
fauna.
40%
-
1%
30%
Frog
40%
5%
-
Case-2
-
60%
1%
80%
-
2%
85%
-
Case-3
5%
-
10%
-
-
10%
5%
-
Case-4
3%
-
5%
1%
Frog
2%
-
Frog
Case-5
1%
-
5%
-
Frog
-
90%
Fish
Case-6
-
10%
1%
20%
Fish
5%
15%
-
Case-7
-
-
-
-
-
-
-
-
Case-8
-
-
-
-
-
80%
-
-
Case-9
15%
-
25%
2%
-
7%
-
-
Case-10
-
-
-
3%
-
-
7%
-
Fauna
Case-1
Epiphyte
(Duckweed)
Fauna
Month-3
Phytoplankton
(Algae)
Month-2
Epiphyte
(Duckweed)
Month-1
Phytoplankton
(Algae)
After
Epiphyte
(Duckweed)
After
Phytoplankton
(Algae)
After
Average nutrient removal efficiencies (Figure 6) show the efficacy of FTWs more clearly
and helped us select optimum design components for the actual pond implementation. The TP
diagram shows that Case-5, which has both littoral zone plants and 5% floating mat coverage,
performed better. Orthophosphate (OP) concentration, Case-9, had a better removal efficiency
with 5% Juncus coverage and no littoral zone. With the same coverage, TN, NO3-N, and NH3-N
20 also had good removal efficiencies in Case-5, 7, 9, and 10. From this observation, we conclude
that 5% coverage of floating mat may suffice for the actual pond.
Figure 6: Average bi-weekly nutrient removal efficiencies.
21 CHAPTER 3: FIELD STUDY
3.1 EXPERIMENTAL DESIGN
A stormwater wet pond located in a community near the University of Central Florida
(UCF) main campus in Orlando, Florida, Pond 5 in this study, was used to investigate the
potential of FTWs. The pond has a surface area around 3,700 ft2 at discharge control elevation
(75.5 ft) and a watershed of about 1.64 acres (Figure 7). Inflow and outflow pipes were both
constructed at the elevation of 72.5 ft. A concrete structure at 71.75 ft in the adjacent wetland
receives the outflow discharge from the pond. It has a 1.25 inch-dia orifice at 75.5 ft and a
fiberglass skimmer top at 76.75 ft, so that when the water level in Pond 5 rises over 76.75 ft, the
flood water will spill away from the top of the concrete structure directly toward the nearby
wetland.
Figure 7: The location of actual pond, Pond 5, in the community
22 3.1.1 Hydrology and Water Balance of Pond 5
A storm event-based water balance for Pond 5 includes the following terms:
∆Storage = Direct Rainfall + Inflow– Evaporation – Infiltration – Outflow
3.1.1.1 Water level
The storage for Pond 5 was represented by water level data, recorded by a water level
sensor (Global Water WL400; Figure 8) installed at the mouth of the circular outlet culvert (i.e.,
0 ft in raw water level data is equivalent to 72.5 ft). The data logger (Global Water GL500-2-1)
was connected with the water level sensor and set to record the water level data at intervals of 10
minutes. The data was exported via its USB port to a laptop computer as an Excel compatible file
(.CSV file)
Figure 8: Water level sensor.
Figure 9: Rain gauge.
3.1.1.2 Rainfall
During the experiment period, rainfall, measured as the direct amount falling into the
pond, was read from a 6-inch Tipping Bucket rain gauge (Figure 9: RG200, Global Water) on
23 site. The radar rainfall data from St. Johns River Water Management District was used as a
backup rainfall data source when the rain gauge was not functioning due to some unpredictable
factors.
3.1.1.3 Inflow
Surface runoff, considered the principal component of the inflow, is the water flow that
occurs when the soil reaches full capacity of water; therefore, the runoff amount depends on the
land size of the watershed that produces runoff flowing into Pond 5. Due to budget limitations,
there was no flowmeter installed at the inlet. Instead, the rational runoff was used to estimate
inflow amount. The watershed area and the runoff coefficient used for the Pond 5 were
summarized (Table 15):
Rational Equation: Q = ciA, where Q = Peak discharge, in cfs; c = Rational method runoff
coefficient; i = Rainfall intensity, in inch/hour; and A = Drainage area, in acres.
Table 15: Watershed area and runoff coefficient used for Pond 5.
Runoff coefficient
(RC) range
RC, used value
Watershed
Area (acre)
weighted runoff fraction
Lawns
0.05-0.35
0.20
0.1950
0.024
Roofs
0.75-0.95
0.85
0.5957
0.309
Concrete streets
0.7-0.95
0.83
0.7615
0.386
Pond
1.00
1.00
0.0849
0.052
1.6371
0.771
Total
24 3.1.1.4 Evaporation
Evaporation, the direct amount evaporated from the pond water surface, is dependent
on many factors, such as temperature, wind, and atmospheric pressure. In our study, an
evaporation pan (Figure 10) located in the UCF stormwater lab was used to measure evaporation
rate, which is further converted to the pond evaporation rate by multiplying by a coefficient of
0.7.
Figure 10: Evaporation pan.
3.1.1.5 Infiltration
It is not feasible to directly measure the infiltration to the groundwater table with time
for the whole pond area; therefore, a period of time when the water level was lower than the
level of orifice on the concrete structure was selected to estimate the infiltration amount. During
that time, the terms of direct rainfall, inflow, and outflow can be considered to be zero, and then
the water balance equation can be simplified as:
∆Storage = – Evaporation – Infiltration
25 That is, the infiltration term can be calculated as the water level loss after subtracting
the evaporation amount. For simplification, the infiltration rate was considered a constant for the
water balance calculation. Once the infiltration term was determined, the outflow term in the
water balance equation was calculated.
3.1.1.6 Outflow
A concrete structure was constructed at 71.75 ft, connecting Pond 5 to the adjacent
wetland. The structure has a 1.25 inch-dia orifice at 75.5 ft and a fiberglass skimmer on the top at
76.75 ft. In other words, when the water level in Pond 5 rises over 75.5 ft, outflow discharges,
and when the water level is higher than 76.75 ft, the flood water spills away from the top of the
concrete structure directly toward the nearby wetland.
3.1.2 Nutrients Removal Performance of FTWs in Pond 5
3.1.2.1 Nutrients removal effectiveness
Water quality analysis was conducted for three storm events and three non-storm events
in first half of July, 2011, as a pre-analysis. Non-storm events analysis was used to produce an
instantaneous snapshot of nutrient distribution throughout the pond and a nutrient reduction
between inlet and outlet. Event-based sampling efforts were carried out in parallel with the nonstorm events sampling campaign. The observation and monitoring during the pre-analysis
provided the background values of stormwater quality and self-purification capacity of the
selected stormwater pond.
To explore nutrient removal efficiencies of BioHaven FTWs, a post-analysis at Pond 5
was carried out for 9 months after the floating wetland deployment. Water quality parameters
26 were monitored to calculate the nutrient removal efficiencies of the FTWs. The post-analysis was
further divided into two parts: non-storm-based and event-based. The data in post-analysis were
used to calculate the additional water quality improvement due to the presence BioHaven
Floating Islands.
3.1.2.2 Operating hydraulic retention time (HRT) and removal efficiencies.
The design HRT is simply considered the ratio of the pond volume and the inflow rate
at design conditions and is simply a ration of the storage volume to an assumed design inflow:
θ=
V
,
Q
where θ = design HRT, d; V = pond volume, m3; and Q = design inflow rate, m3/d.
Removal efficiency is primarily dependent on the pond’s HRT, which is the length of
time that runoff remains in the pond. However, the operating HRT is not equivalent to the design
HRT because the inflow pattern varies over time, never being constant, and the pond volume
continually decreases due to the accumulation of the sediments; therefore, the operating HRT
must be redefined. Forty (40) studies were selected for inclusion in a data base to identify runoff
event mean concentration (EMC) values for single land use categories in Florida (Harper, 2011).
2.102 mg.L-1 for TN and 0.497 mg.L-1 for TP (particulate plus dissolved), the geometric average
of Multi-Family Residential Runoff characterization data, were used as the initial nutrients
concentration in the runoff received by the Pond 5.
Since the event-based sampling efforts were carried out in parallel with the monthly
sampling campaign, the operating HRT is defined as: (1) time interval between the occurrence of
27 the storm and the storm event time point of sampling (and conversion to a daily basis as a matter
of convenience) and (2) time interval on the daily basis between the end of last storm event and
the time point of the subsequent non-storm sampling. Therefore, the event-based data would
reveal how much of nutrients were removed by the physical sedimentation process within a short
HRT (event based) and the monthly based data would imply how much of nutrients were
removed by the biological treatment during a long HRT. Removal efficiency would vary with
different operating HRT. Thus, a plot of operating HRT vs. removal efficiencies would be
produced to provide another perspective of nutrients removal performance of FTWs.
3.1.2.3 Credit of FTWs
Besides the self-purification capacity via natural process, floating wetlands are
introduced to further improve the water quality, which is essential to quantify additional credit
for floating wetlands in terms of (1) assumed value based (outlet value vs. assumed runoff value)
and (2) inlet value based (outlet value vs. inlet value) nutrient control.. It should be recognized
that particulates will most likely settle out during a short HRT and floating islands could hardly
help remove particulates, but mostly dissolved fraction of nitrogen and phosphorus by a
biological way in a relatively long-term period.
Thus, the procedure for assessing the
performance credit of floating wetlands can be described below:
(1) Assumed value based
A) Short-term settling dominated removal efficiency (REs);
28 RES =
Cassumed − CI −S
× 100%
Cassumed
Note: Assume input of TN is 2.102 mg.L-1 and TP is 0.497 mg.L-1; C I − S : Geometric
mean of nutrients concentration at the inlet in the storm events
B) Overall removal efficiency (REO);
REO =
Cassumed − CO− N
× 100%
Cassumed
Note: C O − N : Geometric mean of nutrients concentration at the outlet in the non-storm
events
C) Long-term biologically dominated removal efficiency (REB);
⎛ C
− CO− N Cassumed − CI −S
RE B = REO − RES = ⎜⎜ assumed
C
Cassumed
assumed
⎝
=
⎞
⎟ × 100%
⎟
⎠
CI −S − CO− N
× 100%
Cassumed
REB in terms of TN and TP would be calculated for both pre-analysis (without FTWs)
and post-analysis (with FTWs) for FWTs. A marginal concentration-based improvement would
be used to estimate the credit of floating wetlands as: REB (with FTWs) – REB (without FTWs)
(2) Inlet value based
29 CI −S − CO− N
× 100%
CI −S
3.2 EXPERIMENTAL SETTINGS
The BioHaven FTWs were deployed at Pond 5 on July 15, 2011. Each of the four
floating islands was an 80 ft2 mat that occupied collectively 5 % of the pond surface area to
ensure the reliability of the engineering practices. The mats were tied together in a ring
surrounding the fountain, away from the inlet and outlet (Figures 11). Plant species were the
same as in the mesocosm study. Pots in the mat were filled with peat moss as the plant substrate.
Figure 11: Deployment of floating wetland (photos taken 7/15/2011).
30 3.3 SAMPLING AND MEASUREMENTS
Sampling locations were at the inflow pipe from the street and the outflow pipe to the
adjacent wetlands (Figure 12). Composite samples were collected during storm and non-storm
events. All the composite samples were stored at 4 °C and delivered to a NELAC certified
laboratory off campus for chemical analysis of nutrients using various methods (Table 16). Note
that the fountain in Pond 5 operated throughout the entire monitoring period.
Figure 12: Sampling locations at the Pond 5 (Google Earth, taken 5/1/2011).
Table 16: Outline of analytical methods.
Parameter
TN
NOx-N
NH3-N
TP
OP
Analytical Method
SM21 4500-N C
EPA 353.2 / SM21 4500-NO3 F
EPA 350.1 / SM21 4500-NH3 G
EPA 365.1 / SM21 4500-P B
EPA 365.1 / SM21 4500-P F
31 3.4 RESULTS AND DISCUSSION
3.4.1 Pre-analysis
The pre-analysis period was defined as the study period before the deployment of the
floating wetland. Within the pre-analysis period, three storm and three non-storm events were
investigated in the first half of July to determine the background of this pond. The concentration
reduction percentage (CRP) results, which compare the nutrient levels at the inlet and outlet
(Figure 13 and Table 17,18) show that for storm events, the nutrient levels for TP and OP in
inflow and outflow were almost the same (Table 17). The nutrient increase from the inlet to the
outlet is most likely caused by the water fountain in the pond. Three forms of nitrogen in the
outflow were even higher than those in the inflow. Low concentrations of NH3 and NO2+NO3
indicate that the dominant N form is organic nitrogen. Yet, the smaller difference in TN levels
between inlet and outlet, along with a positive CRP of TP, OP, NH3, and nitrite +nitrate-nitrogen
(NO2-N +NO3-N), indicates that a moderate self-purification occurred in Pond 5. In non-storm
events, organic nitrogen was partially converted to NH3, leading to the increase of NO2+NO3 due
to the aeration by the fountain when compared to the counterparts in storm event.
CRP =
Cinlet − Coutlet
× 100%
Cinlet
32 Table 17: Nutrients concentration for storm events during pre-analysis (mg.L−1).
Date
TP
In
OP
Out
In
TN
Out
In
Out
NO2+NO3
NH3
In
In
Out
Out 7/2/11
0.032 0.034 0.008 0.008 0.223 0.332 0.011 0.032 0.012 0.009 7/7/11
0.030 0.032 0.009 0.009 0.427 0.528 0.003 0.017 0.008 0.001 7/12/11
0.023 0.016 0.001 0.001 0.251 0.272 0.005 0.003 0.123 0.146 Average 0.028 0.027 0.006 0.006 0.300 0.377 0.006 0.017 0.048 0.052 CRP, % 3.5
0.0
-25.6
-173.7
-9.1
Table 18: Nutrients concentration for non-storm events during pre-analysis (mg.L−1).
Date
TP
In
OP
Out
In
TN
Out
In
Out
NO2+NO3
NH3
In
In
Out
Out
7/8/11
0.044 0.038 0.003 0.002 0.362 0.388 0.054 0.045 0.149 0.114
7/9/11
0.040 0.036 0.004 0.002 0.265 0.302 0.007 0.016 0.114 0.110
7/11/11
0.026 0.027 0.001 0.001 0.281 0.358 0.015 0.006 0.100 0.086
Average 0.037 0.034 0.003 0.002 0.303 0.349 0.025 0.022 0.121 0.103
CRP, % 8.2
37.5
-15.4
33 11.8
14.6
a) storm events
b) non-storm events Figure 13: Nutrients concentration during pre-analysis.
34 3.4.2 Post-analysis
The post-analysis period is defined as the study period after the deployment of the FTW.
During the post-analysis period, in-situ data for water quality analysis at Pond 5 were monitored
continuously to test if the deployment would function as we expected in the two scenarios: storm
versus non-storm events. Water samples in four storm and four non-storm events were collected,
and nutrient samples were delivered to the same certified laboratory off campus for chemical
analysis. The overall performance of the BioHaven Floating Island FTW between storm and nonstorm events were investigated and compared between the pre-analysis and post-analysis
conditions. Attention was still placed upon the performance differentiation of the BioHaven
Floating Island between storm and non-storm events.
Six storm events were monitored after the deployment on August 16th and 28th,
September 19th, October 8th and 29th, 2011 and April 6th 2012. The nutrient levels in runoff
during post-analysis (Table 19) were much higher than those during pre-analysis (Table 17);
even so, high removal of TN and NO2+NO3 was observed (Figure 14a), which confirms the
credit of the FTWs performance. In addition to the analysis for storm events, sampling for eight
non-storm events was carried out on July 27th, August 23rd, September 2nd, November 17th,
December 14th, 2011, February 2nd and March 27th, 2012. Positive removal was observed in
terms of all forms of nutrients (Figure 14b). The overall CRP of phosphorus was substantial:
46.3% TP and 79.5% OP were removed, probably by the combination of adsorption through peat
moss in the floating wetlands and sedimentary process in the pond. The removal of TN,
NO2+NO3, and NH3 reached 16.9, 16.7, and 53.0 %, respectively. Overall, significant
improvements were found in post-analysis (Tables 19 and 20). The only exception is the
35 presence of a negative TP removal in one storm event due to the variation of TP concentration in
the outflow during the disturbance of the storm (Table 19).
Table 19: Nutrients concentration for storm events during post-analysis (mg.L−1).
TP
In
Out
8/16/11
0.052
0.035
8/28/11
0.015
0.046
9/19/11
0.004
0.027
10/8/11
0.053
0.055
10/29/11 0.035
0.038
4/6/12
0.160
0.060
Average 0.053
0.042
CRP, %
21.3
Date
OP
In
Out
0
0.001
0
0.001
0
0.002
0.028 0.027
0
0.001
0.094 0.036
0.020 0.010
51.5
TN
In
Out
0.853 0.645
0.638 0.431
0.465 0.649
0.324 0.320
0.253 0.215
0.941 0.455
0.579 0.505
12.7
NO2+NO3
In
Out
0.194 0.017
0
0.003
0.006 0.007
0.056 0.049
0.054 0.01
0.008 0.004
0.053 0.020
62.3
NH3
In
Out
0.186 0.325
0.194 0.159
0.073 0.143
0.044 0.043
0.034 0.026
0.092 0.003
0.104 0.100
3.3
Table 20: Nutrients concentration for non-storm events during post-analysis (mg.L−1).
Date
TP
OP
TN
7/27/11
8/23/11
9/2/11
11/17/11
12/14/11
2/2/12
3/27/12
Average
CRP, %
In
Out
0.196 0.033
0.031 0.028
0.093 0.054
0.017 0.02
0.052 0.037
0.030 0.028
0.018 0.013
0.057 0.030
46.3
In
Out
0.112 0.001
0.002 0.004
0.039
0
0.001 0.001
0.025 0.011
0.019 0.016
0.000 0.000
0.023 0.005
79.5
In
Out
1.154 0.481
0.514 0.542
0.841 0.751
0.47 0.827
0.780 0.512
0.737 0.611
0.151 0.150
0.666 0.553
16.9
36 NO2+NO3
In
Out
0.028 0.02
0
0
0.014
0
0.061 0.063
0.032 0.043
0.012 0.014
0.094 0.060
0.034 0.029
16.7
NH3
In
Out
0.468 0.137
0.169 0.176
0.447 0.348
0.017 0.029
0.183 0.022
0.011 0.009
0.017 0.016
0.224 0.105
53.0
a) storm event
b) non-storm event
Figure 14: Nutrients concentration during post-analysis.
37 3.4. 3 Operating HRT and removal efficiencies
Table 21 and 22 summarize the operating HRT associated with nutrients removal
efficiencies during the post-analysis for Pond 5 study. Similarly, the logarithmic trend looks
apparent in figure 57 and 58 that longer operating HRT leads to higher removal efficiencies.
During post-analysis, TP removal was stable over 68% when the operating HRT was longer than
a few hours. In comparison, TN removal was a more complicated dynamic process due to the
involvement of nitrogen and denitrification processes. Further, the operation of the fountain
introduced more dissolved oxygen, interrupting denitrification and sedimentation, both of which
influence the removal of TN, leading to the decreased removal efficiencies with longer operating
HRT.
Table 21: Operating HRT associated with TN removal efficiencies.
Event-based
Monthly-based
Sampling date (dd-mm-yy) Operating HRT, d TN, mg L-1 Removal, %
16-08-11
28-08-11
19-09-11
08-10-11
29-10-11
06-04-12
27-07-11
23-08-11
02-09-11
17-11-11
14-12-11
02-02-12
27-03-12
0.06
N/A*
N/A*
N/A*
0.43
0.02
4.0
3.8
1.9
17
27
37
16
38 0.853
0.638
0.465
0.324
0.253
0.941
0.481
0.542
0.751
0.470
0.512
0.611
0.150
59.4
69.6
77.9
84.6
88.0
55.2
77.1
74.2
64.3
77.6
75.6
70.9
92.9
Table 22: Operating HRT associated with TP removal efficiencies.
Sampling date (dd-mm-yy) Operating HRT, d TP, mg L-1 Removal, %
16-08-11
0.06
0.052
89.5
28-08-11
N/A*
0.015
97.0
19-09-11
N/A*
0.004
99.2
08-10-11
N/A*
0.053
89.3
29-10-11
0.43
0.035
93.0
06-04-12
0.02
0.160
67.8
26-04-12
N/A*
0.030
94.0
15-05-12
0.04
0.116
76.7
Monthly-based
27-07-11
4.0
0.033
93.4
Event-based
23-08-11
02-09-11
17-11-11
14-12-11
02-02-12
27-03-12
21-05-12
3.8
1.9
17
27
37
16
2.0
39 0.028
0.054
0.02
0.037
0.028
0.013
0.015
94.4
89.1
96.0
92.6
94.4
97.4
97.0
Figure 15: Operating HRT vs. TN removal in a pond with a water fountain and FTWs
Figure 16: Operating HRT vs. TP removal in a pond with a water fountain and FTWs.
40 3.4.4 Credit of FTWs
In addition to flood control and downstream erosion prevention, nutrient removal
becomes a major function of stormwater wet detention ponds. Besides its self-purification
capacity via natural process, floating wetland technology is introduced to further improve the
water quality. Noted in the sampling of the influent is the type of sampling minimizes the
inclusion of particulate material. This is done so that it is recognized that particulates will most
likely settle out and floating islands do not remove particulates, but only dissolved fraction of
nitrogen and phosphorus. Table 23 summarizes the credit estimation for the FTWs.
Table 23: Credit of FTWs in Pond 5 with a Water Fountain
TN
TP
Without FTW
With FTW
Without FTW
With FTW
C I −S
0.288
0.519
0.028
0.031
CO− N
0.347
0.498
0.033
0.028
REB (%) Runoff
**
Concentration
based *
Credit (%)
-2.83
(-1.78 – -4.49)
0.99
(0.63 – 1.58)
-1.06
(-0.76 – -1.46)
0.65
(0.47 – 0.90)
RE (%)
Pond
Concentration
Credit (%)
based
-20.6
3.82
(2.41 – 6.06)
4.0
24.6
* Multi-Family Residential Land Use
** Geometric Average and (± 1 Standard Deviation)
41 1.71
(1.23 – 2.37)
-18.7
10.4
29.1
CHAPTER 4 CONCLUSIONS
In the mesocosm study, 11 cases were fully tested for the selection of design parameters
in the field campaign. We concluded that a 5% coverage of the floating mat can achieve a
significant amount of nutrient removal efficiency within a 15-day time span when the initial
concentration is approximately 1 mg.L−1 phosphate and 3 mg.L−1 nitrate. More area coverage
will be suitable from engineering perspective if the HRT is less than 15 days. From an ecological
point of view, FTWs can suppress algae and duckweed growth significantly, which may harm
the fish populations and create aesthetic issues in stormwater management in wet ponds. Ease of
harvesting is another advantage of this FTW system, which is important because the full
vegetation cycle involves return of most nutrients from senescing and decomposing. The
inclusion of a littoral zone and bottom sediments is critical because they regulate the metabolism
of the entire ecosystem in the pond. Nevertheless, additional studies are needed with typical
wetland hydrologic characteristics in an actual pond with different types of vegetation or floating
mats to better understand the efficacy of FTWs. Although this grouped mesocosm study clearly
showed the probable evolution of unwanted plant species, which enriches the knowledge for the
practitioners of FTWs, the possible contribution based on short-term uptake measurements in
wetland and aquatic systems without accounting for seasonality effect would not be
representative.
To better understand the performance of BioHaven FTWs, an actual pond study was
launched at Pond 5 with around 5 % coverage of floating wetland. Promising potential supports
more real world applications. Although the nutrient level in storm runoff during post-analysis
was much higher than that in pre-analysis, positive removal of TN and NO2-N+NO3-N was
42 observed as compared to the counterparts in pre-analysis. For the non-storm events, the
phosphorus removal was substantial. About 46.3 % TP and 79.5% OP were removed, probably
by the sedimentation. The removal of TN, NO2-N+NO3-N, and NH3 also achieved 16.9, 16.7,
and 53.0 %, respectively. The removal efficiency of different phases was compared to assess
additional credit of BioHaven FTWs. The specific term “Operating HRT” was defined to
demonstrate the FTWs performance in Pond 5. The longer operating HRT generally leads to
higher TP but not TN removal efficiencies. Based on the approach for evaluating the
performance credit of floating wetlands, additional 2.41 – 6.06 % of TN and 1.23 – 2.37 % of TP
(in terms of marginal concentration improvement) and 24.6 % of TN and 29.1 % of TP were
removed by BioHaven FTWs, according to the assumed value based and inlet value based credit,
respectively., however. The floating wetlands have a relatively stable performance on the
marginal TN improvement and play a positive role in support of the sedimentation by reducing
the disturbance of the fountain operation to some extent. It has been suggested that harvesting of
plants may increase removal rates by keeping the plants in a high growth mode. However,
harvesting must be performed on a rotational basis in order to maintain a sufficient standing crop
of vegetation. The efficacy of harvesting rooted macrophytes and the overall impact on removal
rates for stormwater has not specifically been investigated and warrants further study.
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