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Assessing sediment removal capacity of vegetated and non-vegetated settling ponds in prawn farms
Aquacultural Engineering 27 (2003) 295 /314 www.elsevier.com/locate/aqua-online
Assessing sediment removal capacity of vegetated and non-vegetated settling ponds in prawn farms Halmar Halide a, Peter V. Ridd a, Eric L. Peterson b,*, David Foster c a
School of Mathematical and Physical Sciences, James Cook University, Townsville, Qld. 4811, Australia b School of Engineering, James Cook University, Townsville, Qld. 4811, Australia c Queensland Department of Primary Industries, Cairns, Qld. 4870, Australia Received 9 April 2000; accepted 1 January 2003
Abstract Sediment removal capacity is assessed for a constructed mangrove wetland, and a nonvegetated settling pond that are both used for filtering water in tropical aquaculture. The assessment is performed through sediment budget analysis using data of suspended sediment concentration collected from optical backscatter sensors. The sensors were deployed at the pond’s inlet and outlet. These data sets provide a measure of trapping efficiency of each pond with different flow regimes and settling areas. The tides influenced flow in the wetland but none was felt in the settling pond. The average trapping efficiency obtained for the vegetated and the non-vegetated ponds was (409/33) and (709/36)%, respectively. The deposition rate calculated for the vegetated and non-vegetated pond ranges between 13 /174 g/m2 per h (average/63 g/m2 per h) and 10 /19 g/m2 per h (average/14 g/m2 per h), respectively. The efficiency of vegetated and non-vegetated ponds is likely to be improved by decreasing the aspect ratio (length/width) from the current value of 6 to 1 and of 5 to 1, respectively. # 2003 Elsevier Science B.V. All rights reserved. Keywords: Shrimp; Mangroves; Settling pond; Sediment removal; Wetland; Trapping efficiency; Deposition
* Corresponding author. Present address: Centre for Marine Studies, University of Queensland, Brisbane, Qld. 4072, Australia. E-mail address: [email protected] (E.L. Peterson). 0144-8609/03/$ - see front matter # 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0144-8609(03)00002-5
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1. Introduction The demand for aquaculture products to supply the growing human population with fish protein is projected to increase (Hardy, 1999). Following the rapid expansion in productivity in the 1980s, progress has slowed because of factors such as deteriorating water quality, and increased occurrence of diseases (Welcomme, 1996). Corea et al. (1998) showed that poor water quality resulting from the selfpollution of aquaculture ponds, i.e. the pond effluent being fed back through the farm intake, is associated with diseases and low-quality aquaculture products. In an effort to maintain water quality standards the industry has pursued the use of water treatment technologies. Some of these techniques have proven to be effective in reducing the occurrence of disease outbreaks and improving survival rates of pondheld organisms (Yamada et al., 1991; Chin and Ong, 1997; Tseng et al., 1998). Thus, in order to sustain this productivity to meet the demand for pond-cultured seafood in the future, it is essential that the high quality of influent/effluent water be maintained. Vegetated wetland systems and non-vegetated settling/sedimentation ponds have both been utilised for water treatment and are considered in this study. The use of constructed wetlands for treating waste-water from various sources such as urban agricultural run-off, and for mining and industrial applications are reported in several edited collections (see for example Hammer, 1989; Moshiri, 1993; Bavor and Mitchell, 1994). From these studies, it was found that the efficiency of pollutant removal is dependent on the characteristics of the pollutants (Reed et al., 1995; Kadlec and Knight, 1996). For instance, an experimental site in Austria reported in Vymazal et al. (1998) had a much higher capacity for removing sediment than nutrients, particularly dissolved nitrogen, from the input waters. In this case, the system was able to retain more than 90% of input sediment whilst removing approximately 50% of input nitrogen. In other studies, similar systems were demonstrably effective in the removal of heavy metals (Watson et al., 1989) and pathogenic organisms (Gersberg et al., 1989). Vegetated wetlands also act as sanctuaries for wildlife (Gearheart and Higley, 1993) and may provide a valuable extension to fisheries habitat within the confines of the farm boundary to fish (Louis et al., 1995). One crucial factor that determined this pollutant removal capacity is known as the aspect ratio, i.e. the length-to-width ratio, of the system. There are considerable differences in the literature about what is the optimal aspect ratio. Some workers have found that the ratio needed should not greater than 83 (Conley et al., 1991; Knight et al., 1993; Brown and Reed, 1994) whereas Watson and Hobson (1989) and Steiner and Freeman (1989) regard a ratio of 1 is an upper limit. Chen et al. (1993) have theoretically derived that a very aspect ratio (0.02) results in lower costs. Wetland systems have also been applied in aquaculture. The wetland plants, i.e. macrophytes, have been reported to provide a supplementary feed source for cultured Penaeus monodon (Catacutan, 1993). The wetland system was also used for reducing BOD and removing toxicity from salmon hatchery wastes (Hunter et al., 1993). Redding et al. (1997) studied the use of three types of macrophytes in a recirculating system for treating nutrient wastes released by fish Nile tilapias. The
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system had three components: a sedimentation unit, a plastic media as biofiltration unit, and an aquatic plant unit. Although this system was very small in size (utilising a flow rate of 20 l/min), removal rates for ammonia, nitrate, and phosphorus were 5.25, 21.61, and 9.5 kg/ha per day, respectively. Other plants, such as mangroves, can also be used in treatment wetlands. The potential use of natural mangrove forests as nutrient-strippers for municipal discharge and aquaculture effluent has recently been demonstrated (Wong et al., 1997). Further, the mangrove area needed for the purpose of removing nutrients has been estimated. Assuming that the only sink for nutrients was the mangroves uptake, Robertson and Phillips (1995) suggested that to remove nitrogen and phosphorus from an effluent released by a 1-ha pond, a Rhizophora forest between 2 and 22-ha was needed. The area needed for plantation could be made much smaller by taking into consideration processes such as denitrification and sedimentation that are responsible for nutrient uptake. Adding these factors into the nitrogen budget analysis, Rivera-Monroy et al. (1999) estimated that only up to 2-ha mangroves (Rhizophora spp.) is required to remove the dissolved inorganic nitrogen (DIN) produced by a 20-ha shrimp pond. Beside their nutrient-stripping capabilities, natural mangrove forests also trap suspended sediments (SS) (Woodroffe, 1992; Furukawa and Wolanski, 1996; Wolanski et al., 2000) and heavy metals (Tam and Wong, 1995; Tam et al., 1995). There are several reasons why sediments deserve attention in relation to water treatment in aquaculture ponds. First, Mokhtar et al. (1994) related almost 40% of weight reduction of prawns due to the contaminated input water to the farms. In this case, the pollutants were excessive suspended solid and heavy metals. Secondly, House et al. (1995) found the capability of suspended solid on removing phosphorous. Rysgaard et al. (1999) also showed that sediment plays an important role in nutrient remineralisation through denitrification. Sarmani (1989) found high concentrations of heavy metals adhered to suspended solids. This adsorption was modelled by Wen et al. (1998) using a surface complexation model. The model successfully described how river sediment absorbed lead (Pb) and Cadmium (Cd). Fourthly, sediment provides a substrate for micro-organisms including pathogenic bacteria and viruses (Husevag et al., 1991; Wiklund, 1995) Various mechanisms and factors affecting virus attachment onto particle surfaces were reviewed in Gerba (1984). In fact, the use of non-vegetated settling pond and removal of silt from the pond resulted in fewer reported diseases (Shang et al., 1998). The use of non-vegetated settling/sedimentation pond for water treatment is a much older technique (Raudkivi, 1993). The theory of settling of discrete particles, i.e. type I settling, and the design of sedimentation tanks, either rectangular or circular, can be traced back to the works of Hazen (1905). In this tank, the removal of particles depend on the overflow velocity (rate of flow per surface area) and not on the detention time and water depth. Later on, Camp (1946) extended the theory to include the case for flocculating particles, i.e. type II settling. Recent studies on settling ponds use a dimensional analysis technique for investigating parameters involved in the settling process. In this method, a dependent variable is expressed in terms of a combination of non-dimensional independent variables. This approach
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was applied by several investigators. For instance, Vittal and Raghav (1997) applied the technique for determining the size of a settling basin in India. They derived their sizing formulae based on the rate of flow, settling efficiency and roughness coefficient. The size calculated using these formulas were close to that of using formulas of Camp (1946). This is expected since both methods use the overflow velocity as an input to calculate the size of a pond. Raju et al. (1999) also developed a non-dimensional relation for calculating settling efficiency of a pond. In their formulae, the settling efficiency EFF is calculated based on water flow and depth, bed roughness, settling velocity, and pond’s dimensions. The relation was a modified version from various formulae developed earlier (Vanoni, 1975; Garde et al., 1990). The formulae has been extensively tested on modeling data from their own laboratory measurements as well as data from previous investigators such as: Dobbins (1944), Camp (1946), Vanoni (1975), Sumer (1977) and Garde et al., (1990). Applying these data sets, the formulae was able to reduce the error (i.e. the difference between computed and observed removal efficiencies) from 40 to 25%. In this study, the primary objective will be focused on assessing the use of a constructed mangrove wetland (CMW) and a non-vegetated settling pond for removing SS in prawn farms. A CMW in Indonesia and a settling pond in Australia were selected for our purpose. The CMW is used for treating influent (input water) from a creek into the prawn farms whereas the settling pond in Australia was used to reduce the level of suspended solids in farm effluent water prior to its release into a creek. Little is known of the efficacy of these systems for the removal of suspended solids. The traditional ad-hoc approach to settling areas, where they have often been retrofitted into available space to reduce an effluent problem, indicates a real need for cost-effective design criteria to reduce sediment concentrations in aquaculture discharge. This assessment was based on the sediment trapping efficiency, measured by Optical Backscattered Sensors (OBS) or nephelometers. These use an infra-red light source which is emitted into the water column. Suspended particles in the water scatter the light, some of which is received by the sensor. This backscattered light signal provides some measure of the concentration of particles in the water column (Ludwig and Hanes, 1990; Conner and de Visser, 1992; Kineke and Sternberg, 1992).
2. Material and methods 2.1. Study areas This study was conducted in two locations. The first site is located in an aquaculture farm in Maros, South Sulawesi province of Indonesia (see Fig. 1). The pond is owned by the Indonesian Government and run by the Research Institute of Coastal Fisheries-BALITKANTA. The farm receives its water from the nearby tidal, Bawanamarana creek, and discharges its effluent to the much smaller Minangalaba creek. These creeks are sufficiently close to each other to allow the farm to selfpollute (i.e. discharge water will contaminate intake water). In an effort to overcome this problem, a CMW was created as a biofilter for treating the input water (Ahmad,
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Fig. 1. CMW at the inlet of an aquaculture site located in Maros, South Sulawesi, Indonesia. This site receives influent from Bawanamarana creek and discharges its effluent into Minangalaba creek. Two OBS or nephelometers were deployed at A and B.
Personal communication). This 1800 m2 CMW was built in a clay-bottomed pond and planted with Rhizopora spp. (1 /5 per m2), that were 2-years-old at the time of experiment (60 /165 cm in height). The CMW was constantly inundated, flow (magnitude and direction) was determined by tides. The water from the CMW was distributed to the whole farm. In order to determine the capacity of this wetland for removing sediment, two OBS units were deployed at its inlet and outlet, i.e. points A and B in Fig. 1. The second site is located in a prawn-farm in far north Queensland, Australia (see Fig. 2). The non-vegetated settlement pond built into sand /clay soil, was not influenced by tide. Water was gravity fed from grow-out ponds into the settlement pond and exited via a pipe located some 1m from the pond bottom. The settlement
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Fig. 2. Settling pond at a prawn farm in Mission Beach, North Queensland, Australia. This pond receives effluent from the farm’s discharge and channels it into a CMW and a creek. Two OBS or nephelometers were deployed at C and D.
pond was 1 m deep and had a total area of 2600 m2, giving a volume when full of approximately 2600 m3. To look at the sediment removal capacity of the settling pond, two OBS sensors were placed at points C (inlet) and D (outlet) of the settling pond (see Fig. 2).
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2.2. Methods The objective of this experiment was to assess the sediment removal capacity and efficiency of the Maros and North Queensland ponds. Self-logging OBS sensors recorded SS concentration in both the in-flowing and out-flowing water. By comparing the magnitude of the sediment fluxes, an estimate of the efficiency of the pond was made. In addition to the sediment flux measurement, some hydraulic quantities such as water depth, velocity and flow rate were determined. 2.2.1. Experimental set up Two OBS sensors were deployed at the inlet and outlet of the CMW for 4 and 17 days in the settlement pond. Biofouling problems are commonly encountered in shallow water environment and could adversely affect this signal. To prevent this, a wiper was provided to clean the sensor (Ridd and Larcombe, 1994) and it can be programmed to activate at a certain time interval. The backscattered signal was stored in a data logger and it can be programmed to take data at any desired sampling frequency. In both experiments, the sampling rate was 60 s. After the experiment, the data was downloaded from the instrument for analyses. Hydraulic measurements and estimation were also taken at the CMW and the settling ponds. Measurements of water depth and surface velocity were taken at the middle of the CMW pond. The water depth was determined by measuring a water level at a bamboo pole. The pole was marked at 1 cm intervals. The surface velocity was determined by taking a travel time of a plastic float between two poles using a stopwatch. The poles were set at 1 m apart. Water depth and velocity measurement were not taken at the settling pond, but the flow rate at its outlet was estimated instead. In a related study (Peterson et al., 2002) flow was accurately measured at a downstream weir. 2.2.2. Data calibration OBS data is calibrated to SSC by taking a number of samples of different SSC and taking an OBS reading using this sample. A calibration curve such as shown in Fig. 2 is then generated. In Fig. 3, the line of best fit has been forced through the point of zero concentration. The standard error on this calibration curve is 0.0063 g/l. Before proceeding, it is worth noting the systematic errors that can occur with calibrating OBS readings to SS. This topic has been dealt with in detail in a recent review paper by Bunt et al. (1999). The slope of the calibration curve can depend upon many parameters such as particle size, sediment type, relative abundance of organic matter and degree of flocculation. Because of the dependence of the OBS response on the sediment type, it is possible that calibration functions can alter by a factor of as much as 10 between two sediment types of radically different particle size (Bunt et al., 1999). In the work reported here, it is fortunate that we are comparing readings that have been made on essentially the same sediment type. The sediment in the in-flowing water is the same as that in the out-flowing water though the out-flowing water may have a slightly smaller average size.
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Fig. 3. Calibration curve relating optical backscattered sensor readings and SS concentration.
2.2.3. Sediment trapping efficiency Having converted the OBS reading into SS, an instantaneous trapping efficiency (EFF) was calculated using: EFF 100
SSin SSout SSin
(1)
Here, SSin and SSout (in g/l) are the input and output SS associated with the pond, respectively. The water flow rate Q (in m3/s) was calculated as: Q aV
(2)
where V is a flow velocity through a cross sectional area a of the discharge into the pond (width times water depth). The mass accumulated, M , was calculated using: M
g EFFSS Qdt in
(3)
where t is the time. Using (2.3), the hourly mass accumulated Mhr was calculated using: Mhr EFFSSin Q3600
(4)
The accumulated mass per unit area per unit time or the deposition rate DR (measured in units of g/m2 per h) was then calculated using:
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DR
Mhr =A 1000
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(5)
Here A is surface area of the pond.
3. Results and discussion 3.1. Constructed mangrove wetland This wetland was created to treat influent water flowing from Bawanamarana creek into ponds for raising prawns and fish. The flow into and out of the wetland was controlled by tides. Both flow depth and surface velocity were measured during a flood stage, i.e. water flowed into the wetland from the creek. The hourly measurements of water depth and surface velocity were taken at the middle of the wetland (Fig. 4). The water depth varied between 40 and 80 cm from low to high tide, respectively. The water speed also varied between 0 (at the low and high tide) and /20 cm/s. From these quantities, the flow-rate Q was calculated using Eq. (2) and also plotted in Fig. 4. In this calculation, the width of the wetland was 20 m. The estimated error for water depth, speed and flow rate measurement is 1.0 cm, 0.5 cm/s and 0.1 m3/s, respectively. The input and output SS were measured at points A and B. Both the input and output reached a concentration of 0.12 g/l. Using SS at points A and B, the wetland EFF is calculated using Eq. (1) and plotted in Fig. 4. From this figure, the trapping efficiency can be seen to vary between 0 and 88%, with a weighted average of (409/33)%. The large efficiency was achieved near high tide and at the lowest velocity around hours 7 and 9. Note that around 5.5 h, the efficiency should be negative. Note also that Fig. 4 shows data from one rising tide only. The average trapping efficiency of the other three rising tides during the experiment was also calculated and an error calculation was made. The trapping efficiencies found were (359/29), (309/27), and (249/18)%. The lowering efficiency due to the increasing water flow brought by heavy rainfall. The deposition rate DR is computed using Eqs. (4) and (5) by taking the value of the wetland’s area of 1800 m2. The resulting DR is shown in Fig. 5. The DR varies from 13 to 174 g/m2 per h with the average of 63 g/m2 per h. The largest DR occurs around hours 7 and 8. The small amount of accumulated sediment at hour 9 was due to a reduction in the level of input SS at that time. 3.2. Settling pond The measured input and output SS at points C and D at the settling pond are plotted in Fig. 6. The input SS reached up to 0.15 g/l. The output SS was stable at a much lower figure /0.03 g/l. Using Eq. (1), the trapping efficiency is also calculated and presented in Fig. 6. The efficiency ranged from 49 to 95%, with an average of (709/36)%. To look at the amount of variation in trapping efficiency, we also compute efficiency for the other 3 days of data. The efficiencies are: (779/25), (749/
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Fig. 4. Measured water depth, speed, flow rate, input and output SS, and calculated trapping efficiency of the mangrove wetland in Indonesia.
28), and (719/30)%. Using the flow discharge of 0.15 m3/s, respectively, and the pond’s area of 2600 m2, the deposition rate was calculated and plotted in Fig. 7. It was found that the deposition rate was much less than that of the wetland in Indonesia, i.e. it varied from 10.5 to 19 g/m2 per h with the average of 14 g/m2 per h. This is possibly due to two factors. First, the much lower flow rate at the settling pond of 0.15 m3/s compared with that of the CMW where for most of the time the flow rate was above 1 m3/s. The low inflow rate produces a low input flux of sediment that is available for deposition. Second, the much larger area of the settling pond of 2600 m2 compared with that of the CMW of 1800 m2.
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Fig. 5. Deposition rate DR or the areal efficiency calculated for the wetland.
Fig. 6. SS measured and calculated trapping efficiency of the settling pond in Australia.
3.3. Discussion It is important to stress that we cannot make a direct comparison between the two types of pond (i.e. vegetated and non-vegetated) because in the two cases studied here, there are many differences other than vegetation. Area, water depth, water chemistry and flow rates also differ considerably between the two sites.
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Fig. 7. Deposition rate DR for the settling pond.
The trapping efficiency of the mangrove wetland may be limited partly due to the presence of turbulence generated by flow around the mangroves. Even though the plant retards flow by friction which promotes settlement (Yang, 1998), they also generate turbulence preventing sediment deposition. The faster the flow, the larger the turbulence. Furukawa and Wolanski (1996) found that at a flow velocity around 20 cm/s, the turbulent velocity was up to three times larger than the mean flow. In our experiment, the flow velocity was often close to this value and as a result the trapping efficiency was also low. Close to slack high tide, however, when the velocity dropped to much less than the 20 cm/s and the water reached its greatest depth, the effect of turbulence decreased, and allowed sediment particles to settle. As a consequence, the trapping efficiency increased. Note that the velocity threshold needed for generating significant turbulence varies for different vegetation. Anderson and Charters (1982) found that for a sub-tidal bushy plant Gelidium nudifrons , the threshold is as low as 6 cm/s, whereas the threshold for seagrass Thalassia testudinum is between 12 and 17 cm/s (Koch and Gust, 1999). In order to increase the trapping efficiency of this mangrove wetland, the flow should ideally be slow and deep. Slower flow can be achieved by reducing the aspect ratio as has been suggested by Chen et al. (1993). Meanwhile, in order to cope with this deep flow
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requirement, the use of Rhizopora spp. is more advantageous than the use of other species such as Avicennia spp. (McKee, 1993; Lee et al., 1996). The experiment conducted in the non-vegetated settling pond resulted in a high trapping efficiency but a relatively low areal deposition rate. This high trapping efficiency is achieved because of the flow rate was very low, i.e. Q /0.15 m3/s compared with over 1 m3/s for the vegetated CMW pond at Maros) and is less turbulent, i.e. the flow was deeper and much slower. The areal deposition rate may be low due to either low sediment load or because the settling area was excessive for the input flow rate. Low sediment load is associated with small flow rate. This areal deposition rate might be increased by either (i) increasing the sediment load (i.e. large flow rate) or (ii) reducing the area. To further investigate each of these possibilities, we will use the settling formulae of Raju et al. (1999). The formulae were derived for calculating efficiency of a settling pond. The formulae of Raju et al. (1999) for calculating the efficiency of a settling pond with an area A is stated as below: EFF 11:7(v=U)0:81 (A=bh)0:23 (D1=6 =n g1=2 )0:98
(6)
where v is the settling velocity, U is the average pond water velocity, b and h is the width and water depth at the approach channel, respectively, D is the water depth at the settling pond, n is the Manning’s coefficient, and g is the gravitational acceleration. We use Eq. (6) to examine how changing flow rate and area affect the areal deposition rate. Let us consider these cases in more detail.
3.3.1. Case (1) the effect of changing flow rate Q We modify Eq. (6) into: EFF K1 Q0:81
(7)
where K1 (Bv)0:81 (A=bh)0:23 D0:97 (1=n g1=2 )0:98
(8)
Here we have substituted into Eq. (6), the velocity U /Q/BD , in which B is the pond’s width. Increasing the flow rate Q by a factor of 10 while other parameters are kept constant in Eq. (7), i.e. K1 is constant, the EFF reduces to (1/10)0.81 /0.15, i.e. the EFF is reduced by a factor of 1/0.15/6.45 from its former value. Using these new values, we recalculate the areal deposition rate. Here we take the new Q /1.5 m3/s (ten times larger than the previous Q which is 0.15 m3/s) and the new value of the EFF is taken by multiplying the previous EFF plotted in Fig. 6 by 0.15. The resulting average deposition rate calculated using Eq. (5) becomes 15.6 g/m2 per h. This is slightly larger than the previous average deposition rate of 14 g/m2 per h, but considering the rough nature of the calculations, the difference may well be negligible.
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3.3.2. Case (2a) the effect of reducing the area of the pond by reducing the length L and keeping the width B constant To calculate the resulting deposition rate caused by a reduction in pond length, we rewrite Eq. (6) into: EFF K2 L0:23
(9)
where K2 (v=Q)0:81 (1=bh)0:23 D0:97 (1=n g1=2 )B1:04
(10)
Now, Eq. (9) states that reducing the length by a factor of 10 while keeping K2 constant results in the EFF of (1/10)0.23 /0.59, i.e. the EFF is reduced by a factor of 1/0.59 /1.69 to about 59% of its previous value. We recalculate the deposition rate using these new values of both EFF and area. We find the deposition rate becomes 823 g/m2 per h. This value is about six times larger than the previous deposition rate of 14 g/m2 per h.
3.3.3. Case (2b) the effect of reducing the area of the pond by reducing the width B and keeping the length L constant To calculate the resulting deposition rate caused by this reduction, we rewrite (2.6) into: EFF K3 B1:04
(11)
where v 0:81 1 0:23 0:97 1 1=2 0:98 0:23 g K3 D L Q bh n
(12)
From Eq. (11) a reduction in the width by a factor of 10 while keeping K3 constant results in low EFF of (1/10)1.04 /0.09, i.e. the EFF is reduced by a factor of 1/0.09 to about 9% of its previous value. We recalculate the deposition rate using these new values of both EFF and area. We find the deposition rate becomes 12.6 g/m2 per h. This value is slightly less than the previous deposition rate of 14 g/m2 per h. Results from case (2a) and (2b) show that reducing the pond’s length L has a greater effect on DR than reducing the width B . We can also demonstrate that this result is still valid by keeping the area constant while changing B and L . For instance, reducing the length by a factor of 2 hence increasing the width by a factor of 2, the deposition rate found was 26 g/m2 per h. On the other hand, increasing the length by a factor of 2 while reducing the width by a factor of 2, the deposition rate found was 7.4 g/m2 per h. This suggests that in order to improve the sediment trapping capacity, the length-to-width ratio or the aspect
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ratio should be made smaller. Note that this idea has been recommended earlier by various investigators such as Chen et al. (1993), Kadlec and Knight (1996) and Platzer and Netter (1994). Further, Platzer and Netter (1994) also suggested the minimum length of 5 m to be used for a wetland system. More recently, applying a numerical method for modeling type I sedimentation, Jin et al. (2000) showed that within a 5 m-length pond the removal efficiency is almost 2/3 of the total removal efficiency. Eq. (6) was validated by using data obtained from experiments in mangrove and settling ponds. The result is given in Table 1, in which minimum and maximum trapping efficiencies are given based on literature values of settling velocity and Manning coefficient. The lower trapping efficiencies occur when very low settling velocities occur, i.e. when very fine clay is involved. From Table 1, it can be seen that the observed efficiency lies close to the lower bound of calculated efficiency. This regime belongs to very fine sediment materials and is consistent with observations from both ponds in which very fine clay particles are observed to settle. It should be noted that the calculated efficiency can be as much as a factor 3/4 orders of magnitudes larger for silt particles as opposed to clay particles. From this it is apparent that in-situ measurements of settling velocity are crucial in the design of efficient settling ponds. More work is needed to develop formulae equivalent to Eq. (6) to increase sediment trapping efficiency in the design of a settling pond and, possibly, of a constructed mangrove wetland. Firstly, finding the optimal aspect ratio for a particular settling pond. Even though, it can be shown from Eq. (6) that efficiency is a linear function of this ratio, in practice, this ratio might not be enlarged indefinitely. A reduction in this ratio reduces may lead to the short-circuiting problem in which the incoming sediment particles do not have enough time to settle thereby lowering the efficiency (Crites, 1994). In order to avoid this problem and to calculate the minimum length of a pond by rearranging Eq. (6) for a certain trapping efficiency to be achieved, one has to estimate the smallest settling velocity of incoming effluent. The best estimate would be to measure the settling velocity in-situ. This will be the subject for the work in the future. Secondly, investigating the use of the above settling formulae for the vegetated constructed wetland systems is recommended. This is possible through the use of appropriate roughness factor, i.e. the Manning’s coefficient n. One way of calculating this coefficient for the case of natural mangrove forest is available in Wolanski et al. (1992). The success of applying or possibly modifying Eq. (6) for the wetland might bring us to better understand why a wide range of width-to-length ratios have been suggested in the literature for wetland design (Knight et al., 1993; Brown and Reed, 1994; Kadlec and Knight, 1996). The observed efficiencies at both sites are based on mean values. The minimum and maximum calculated efficiency is found using the settling velocity w of 24 / 107 and 24/105 m/s (Heltzel and Teeter, 1987), respectively. The velocity covers clay and silt material commonly found in both sites. The Manning coefficient n used in the mangrove wetland and the settling pond is 0.2 s/m1/3 (Wolanski et al., 1992) and 0.02 s/m1/3 (Chow, 1959), respectively.
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Site
Water Depth velocity (m) (m/ s)
Manning roughness Area Trapping efficiency (s/ m 1/3) (m2) (%) Observed Calculated using Eq. (6) with w of 24/10 7 and 24/10 5 m/s
CMW Non-vegetated settling pond
0.02 0.005
0.4 /0.8 0.2 1.0 0.02
1800 (309/25) 2600 (749/28)
Minimum
Maximum
21.3 91
888 3787
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Table 1 Trapping efficiency comparison between observation and calculation using formulae Eq. (6)
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4. Conclusion The sediment removal capacity of a CMW and a settling pond from tropical aquaculture ponds has been investigated. The CMW and the settling pond are intended to improve water quality of the influent and effluent, respectively. The trapping capacity observed in the wetland could be improved by slowing the water speed to less than 10 cm/s. Higher speed promotes turbulence, which is in turn, hinders the sediment deposition. The higher sediment retention found in the settling pond is achieved through deeper and slower water flow. It is likely that efficiency of the pond would be improved by using a shorter but wider pond.
Acknowledgements We thank the Director of RICA pond in Maros, South Sulawesi Indonesia for letting us its constructed mangrove wetland. We also would like to thank three anonymous reviewers, Professor D. Karoly and Dr A.D. Reyes Jr. for their suggestion to put uncertainty and to validate our results. HH would like to express his gratitude to the Australian Government for the AUSAID Ph.D. scholarship. Lead author Dr Halmar Halide is now teaching in the Physics Department, Hasanuddin University Jl, Kandea 26 Baraya, Sulawesi Selatan, Makassar 90153, Indonesia. Co-authors are obliged for the lead author’s re-working of the manuscript at his home institution, without any access to internet. HH is now making a weekly trip to an internet cafe´ and searching for work in meteorological forecasting with his fuzzy logic tools, via [email protected]
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Assessing sediment removal capacity of vegetated and non-vegetated settling ponds in prawn farms Halmar Halide a, Peter V. Ridd a, Eric L. Peterson b,*, David Foster c a
School of Mathematical and Physical Sciences, James Cook University, Townsville, Qld. 4811, Australia b School of Engineering, James Cook University, Townsville, Qld. 4811, Australia c Queensland Department of Primary Industries, Cairns, Qld. 4870, Australia Received 9 April 2000; accepted 1 January 2003
Abstract Sediment removal capacity is assessed for a constructed mangrove wetland, and a nonvegetated settling pond that are both used for filtering water in tropical aquaculture. The assessment is performed through sediment budget analysis using data of suspended sediment concentration collected from optical backscatter sensors. The sensors were deployed at the pond’s inlet and outlet. These data sets provide a measure of trapping efficiency of each pond with different flow regimes and settling areas. The tides influenced flow in the wetland but none was felt in the settling pond. The average trapping efficiency obtained for the vegetated and the non-vegetated ponds was (409/33) and (709/36)%, respectively. The deposition rate calculated for the vegetated and non-vegetated pond ranges between 13 /174 g/m2 per h (average/63 g/m2 per h) and 10 /19 g/m2 per h (average/14 g/m2 per h), respectively. The efficiency of vegetated and non-vegetated ponds is likely to be improved by decreasing the aspect ratio (length/width) from the current value of 6 to 1 and of 5 to 1, respectively. # 2003 Elsevier Science B.V. All rights reserved. Keywords: Shrimp; Mangroves; Settling pond; Sediment removal; Wetland; Trapping efficiency; Deposition
* Corresponding author. Present address: Centre for Marine Studies, University of Queensland, Brisbane, Qld. 4072, Australia. E-mail address: [email protected] (E.L. Peterson). 0144-8609/03/$ - see front matter # 2003 Elsevier Science B.V. All rights reserved. doi:10.1016/S0144-8609(03)00002-5
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1. Introduction The demand for aquaculture products to supply the growing human population with fish protein is projected to increase (Hardy, 1999). Following the rapid expansion in productivity in the 1980s, progress has slowed because of factors such as deteriorating water quality, and increased occurrence of diseases (Welcomme, 1996). Corea et al. (1998) showed that poor water quality resulting from the selfpollution of aquaculture ponds, i.e. the pond effluent being fed back through the farm intake, is associated with diseases and low-quality aquaculture products. In an effort to maintain water quality standards the industry has pursued the use of water treatment technologies. Some of these techniques have proven to be effective in reducing the occurrence of disease outbreaks and improving survival rates of pondheld organisms (Yamada et al., 1991; Chin and Ong, 1997; Tseng et al., 1998). Thus, in order to sustain this productivity to meet the demand for pond-cultured seafood in the future, it is essential that the high quality of influent/effluent water be maintained. Vegetated wetland systems and non-vegetated settling/sedimentation ponds have both been utilised for water treatment and are considered in this study. The use of constructed wetlands for treating waste-water from various sources such as urban agricultural run-off, and for mining and industrial applications are reported in several edited collections (see for example Hammer, 1989; Moshiri, 1993; Bavor and Mitchell, 1994). From these studies, it was found that the efficiency of pollutant removal is dependent on the characteristics of the pollutants (Reed et al., 1995; Kadlec and Knight, 1996). For instance, an experimental site in Austria reported in Vymazal et al. (1998) had a much higher capacity for removing sediment than nutrients, particularly dissolved nitrogen, from the input waters. In this case, the system was able to retain more than 90% of input sediment whilst removing approximately 50% of input nitrogen. In other studies, similar systems were demonstrably effective in the removal of heavy metals (Watson et al., 1989) and pathogenic organisms (Gersberg et al., 1989). Vegetated wetlands also act as sanctuaries for wildlife (Gearheart and Higley, 1993) and may provide a valuable extension to fisheries habitat within the confines of the farm boundary to fish (Louis et al., 1995). One crucial factor that determined this pollutant removal capacity is known as the aspect ratio, i.e. the length-to-width ratio, of the system. There are considerable differences in the literature about what is the optimal aspect ratio. Some workers have found that the ratio needed should not greater than 83 (Conley et al., 1991; Knight et al., 1993; Brown and Reed, 1994) whereas Watson and Hobson (1989) and Steiner and Freeman (1989) regard a ratio of 1 is an upper limit. Chen et al. (1993) have theoretically derived that a very aspect ratio (0.02) results in lower costs. Wetland systems have also been applied in aquaculture. The wetland plants, i.e. macrophytes, have been reported to provide a supplementary feed source for cultured Penaeus monodon (Catacutan, 1993). The wetland system was also used for reducing BOD and removing toxicity from salmon hatchery wastes (Hunter et al., 1993). Redding et al. (1997) studied the use of three types of macrophytes in a recirculating system for treating nutrient wastes released by fish Nile tilapias. The
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system had three components: a sedimentation unit, a plastic media as biofiltration unit, and an aquatic plant unit. Although this system was very small in size (utilising a flow rate of 20 l/min), removal rates for ammonia, nitrate, and phosphorus were 5.25, 21.61, and 9.5 kg/ha per day, respectively. Other plants, such as mangroves, can also be used in treatment wetlands. The potential use of natural mangrove forests as nutrient-strippers for municipal discharge and aquaculture effluent has recently been demonstrated (Wong et al., 1997). Further, the mangrove area needed for the purpose of removing nutrients has been estimated. Assuming that the only sink for nutrients was the mangroves uptake, Robertson and Phillips (1995) suggested that to remove nitrogen and phosphorus from an effluent released by a 1-ha pond, a Rhizophora forest between 2 and 22-ha was needed. The area needed for plantation could be made much smaller by taking into consideration processes such as denitrification and sedimentation that are responsible for nutrient uptake. Adding these factors into the nitrogen budget analysis, Rivera-Monroy et al. (1999) estimated that only up to 2-ha mangroves (Rhizophora spp.) is required to remove the dissolved inorganic nitrogen (DIN) produced by a 20-ha shrimp pond. Beside their nutrient-stripping capabilities, natural mangrove forests also trap suspended sediments (SS) (Woodroffe, 1992; Furukawa and Wolanski, 1996; Wolanski et al., 2000) and heavy metals (Tam and Wong, 1995; Tam et al., 1995). There are several reasons why sediments deserve attention in relation to water treatment in aquaculture ponds. First, Mokhtar et al. (1994) related almost 40% of weight reduction of prawns due to the contaminated input water to the farms. In this case, the pollutants were excessive suspended solid and heavy metals. Secondly, House et al. (1995) found the capability of suspended solid on removing phosphorous. Rysgaard et al. (1999) also showed that sediment plays an important role in nutrient remineralisation through denitrification. Sarmani (1989) found high concentrations of heavy metals adhered to suspended solids. This adsorption was modelled by Wen et al. (1998) using a surface complexation model. The model successfully described how river sediment absorbed lead (Pb) and Cadmium (Cd). Fourthly, sediment provides a substrate for micro-organisms including pathogenic bacteria and viruses (Husevag et al., 1991; Wiklund, 1995) Various mechanisms and factors affecting virus attachment onto particle surfaces were reviewed in Gerba (1984). In fact, the use of non-vegetated settling pond and removal of silt from the pond resulted in fewer reported diseases (Shang et al., 1998). The use of non-vegetated settling/sedimentation pond for water treatment is a much older technique (Raudkivi, 1993). The theory of settling of discrete particles, i.e. type I settling, and the design of sedimentation tanks, either rectangular or circular, can be traced back to the works of Hazen (1905). In this tank, the removal of particles depend on the overflow velocity (rate of flow per surface area) and not on the detention time and water depth. Later on, Camp (1946) extended the theory to include the case for flocculating particles, i.e. type II settling. Recent studies on settling ponds use a dimensional analysis technique for investigating parameters involved in the settling process. In this method, a dependent variable is expressed in terms of a combination of non-dimensional independent variables. This approach
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was applied by several investigators. For instance, Vittal and Raghav (1997) applied the technique for determining the size of a settling basin in India. They derived their sizing formulae based on the rate of flow, settling efficiency and roughness coefficient. The size calculated using these formulas were close to that of using formulas of Camp (1946). This is expected since both methods use the overflow velocity as an input to calculate the size of a pond. Raju et al. (1999) also developed a non-dimensional relation for calculating settling efficiency of a pond. In their formulae, the settling efficiency EFF is calculated based on water flow and depth, bed roughness, settling velocity, and pond’s dimensions. The relation was a modified version from various formulae developed earlier (Vanoni, 1975; Garde et al., 1990). The formulae has been extensively tested on modeling data from their own laboratory measurements as well as data from previous investigators such as: Dobbins (1944), Camp (1946), Vanoni (1975), Sumer (1977) and Garde et al., (1990). Applying these data sets, the formulae was able to reduce the error (i.e. the difference between computed and observed removal efficiencies) from 40 to 25%. In this study, the primary objective will be focused on assessing the use of a constructed mangrove wetland (CMW) and a non-vegetated settling pond for removing SS in prawn farms. A CMW in Indonesia and a settling pond in Australia were selected for our purpose. The CMW is used for treating influent (input water) from a creek into the prawn farms whereas the settling pond in Australia was used to reduce the level of suspended solids in farm effluent water prior to its release into a creek. Little is known of the efficacy of these systems for the removal of suspended solids. The traditional ad-hoc approach to settling areas, where they have often been retrofitted into available space to reduce an effluent problem, indicates a real need for cost-effective design criteria to reduce sediment concentrations in aquaculture discharge. This assessment was based on the sediment trapping efficiency, measured by Optical Backscattered Sensors (OBS) or nephelometers. These use an infra-red light source which is emitted into the water column. Suspended particles in the water scatter the light, some of which is received by the sensor. This backscattered light signal provides some measure of the concentration of particles in the water column (Ludwig and Hanes, 1990; Conner and de Visser, 1992; Kineke and Sternberg, 1992).
2. Material and methods 2.1. Study areas This study was conducted in two locations. The first site is located in an aquaculture farm in Maros, South Sulawesi province of Indonesia (see Fig. 1). The pond is owned by the Indonesian Government and run by the Research Institute of Coastal Fisheries-BALITKANTA. The farm receives its water from the nearby tidal, Bawanamarana creek, and discharges its effluent to the much smaller Minangalaba creek. These creeks are sufficiently close to each other to allow the farm to selfpollute (i.e. discharge water will contaminate intake water). In an effort to overcome this problem, a CMW was created as a biofilter for treating the input water (Ahmad,
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Fig. 1. CMW at the inlet of an aquaculture site located in Maros, South Sulawesi, Indonesia. This site receives influent from Bawanamarana creek and discharges its effluent into Minangalaba creek. Two OBS or nephelometers were deployed at A and B.
Personal communication). This 1800 m2 CMW was built in a clay-bottomed pond and planted with Rhizopora spp. (1 /5 per m2), that were 2-years-old at the time of experiment (60 /165 cm in height). The CMW was constantly inundated, flow (magnitude and direction) was determined by tides. The water from the CMW was distributed to the whole farm. In order to determine the capacity of this wetland for removing sediment, two OBS units were deployed at its inlet and outlet, i.e. points A and B in Fig. 1. The second site is located in a prawn-farm in far north Queensland, Australia (see Fig. 2). The non-vegetated settlement pond built into sand /clay soil, was not influenced by tide. Water was gravity fed from grow-out ponds into the settlement pond and exited via a pipe located some 1m from the pond bottom. The settlement
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Fig. 2. Settling pond at a prawn farm in Mission Beach, North Queensland, Australia. This pond receives effluent from the farm’s discharge and channels it into a CMW and a creek. Two OBS or nephelometers were deployed at C and D.
pond was 1 m deep and had a total area of 2600 m2, giving a volume when full of approximately 2600 m3. To look at the sediment removal capacity of the settling pond, two OBS sensors were placed at points C (inlet) and D (outlet) of the settling pond (see Fig. 2).
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2.2. Methods The objective of this experiment was to assess the sediment removal capacity and efficiency of the Maros and North Queensland ponds. Self-logging OBS sensors recorded SS concentration in both the in-flowing and out-flowing water. By comparing the magnitude of the sediment fluxes, an estimate of the efficiency of the pond was made. In addition to the sediment flux measurement, some hydraulic quantities such as water depth, velocity and flow rate were determined. 2.2.1. Experimental set up Two OBS sensors were deployed at the inlet and outlet of the CMW for 4 and 17 days in the settlement pond. Biofouling problems are commonly encountered in shallow water environment and could adversely affect this signal. To prevent this, a wiper was provided to clean the sensor (Ridd and Larcombe, 1994) and it can be programmed to activate at a certain time interval. The backscattered signal was stored in a data logger and it can be programmed to take data at any desired sampling frequency. In both experiments, the sampling rate was 60 s. After the experiment, the data was downloaded from the instrument for analyses. Hydraulic measurements and estimation were also taken at the CMW and the settling ponds. Measurements of water depth and surface velocity were taken at the middle of the CMW pond. The water depth was determined by measuring a water level at a bamboo pole. The pole was marked at 1 cm intervals. The surface velocity was determined by taking a travel time of a plastic float between two poles using a stopwatch. The poles were set at 1 m apart. Water depth and velocity measurement were not taken at the settling pond, but the flow rate at its outlet was estimated instead. In a related study (Peterson et al., 2002) flow was accurately measured at a downstream weir. 2.2.2. Data calibration OBS data is calibrated to SSC by taking a number of samples of different SSC and taking an OBS reading using this sample. A calibration curve such as shown in Fig. 2 is then generated. In Fig. 3, the line of best fit has been forced through the point of zero concentration. The standard error on this calibration curve is 0.0063 g/l. Before proceeding, it is worth noting the systematic errors that can occur with calibrating OBS readings to SS. This topic has been dealt with in detail in a recent review paper by Bunt et al. (1999). The slope of the calibration curve can depend upon many parameters such as particle size, sediment type, relative abundance of organic matter and degree of flocculation. Because of the dependence of the OBS response on the sediment type, it is possible that calibration functions can alter by a factor of as much as 10 between two sediment types of radically different particle size (Bunt et al., 1999). In the work reported here, it is fortunate that we are comparing readings that have been made on essentially the same sediment type. The sediment in the in-flowing water is the same as that in the out-flowing water though the out-flowing water may have a slightly smaller average size.
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Fig. 3. Calibration curve relating optical backscattered sensor readings and SS concentration.
2.2.3. Sediment trapping efficiency Having converted the OBS reading into SS, an instantaneous trapping efficiency (EFF) was calculated using: EFF 100
SSin SSout SSin
(1)
Here, SSin and SSout (in g/l) are the input and output SS associated with the pond, respectively. The water flow rate Q (in m3/s) was calculated as: Q aV
(2)
where V is a flow velocity through a cross sectional area a of the discharge into the pond (width times water depth). The mass accumulated, M , was calculated using: M
g EFFSS Qdt in
(3)
where t is the time. Using (2.3), the hourly mass accumulated Mhr was calculated using: Mhr EFFSSin Q3600
(4)
The accumulated mass per unit area per unit time or the deposition rate DR (measured in units of g/m2 per h) was then calculated using:
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DR
Mhr =A 1000
303
(5)
Here A is surface area of the pond.
3. Results and discussion 3.1. Constructed mangrove wetland This wetland was created to treat influent water flowing from Bawanamarana creek into ponds for raising prawns and fish. The flow into and out of the wetland was controlled by tides. Both flow depth and surface velocity were measured during a flood stage, i.e. water flowed into the wetland from the creek. The hourly measurements of water depth and surface velocity were taken at the middle of the wetland (Fig. 4). The water depth varied between 40 and 80 cm from low to high tide, respectively. The water speed also varied between 0 (at the low and high tide) and /20 cm/s. From these quantities, the flow-rate Q was calculated using Eq. (2) and also plotted in Fig. 4. In this calculation, the width of the wetland was 20 m. The estimated error for water depth, speed and flow rate measurement is 1.0 cm, 0.5 cm/s and 0.1 m3/s, respectively. The input and output SS were measured at points A and B. Both the input and output reached a concentration of 0.12 g/l. Using SS at points A and B, the wetland EFF is calculated using Eq. (1) and plotted in Fig. 4. From this figure, the trapping efficiency can be seen to vary between 0 and 88%, with a weighted average of (409/33)%. The large efficiency was achieved near high tide and at the lowest velocity around hours 7 and 9. Note that around 5.5 h, the efficiency should be negative. Note also that Fig. 4 shows data from one rising tide only. The average trapping efficiency of the other three rising tides during the experiment was also calculated and an error calculation was made. The trapping efficiencies found were (359/29), (309/27), and (249/18)%. The lowering efficiency due to the increasing water flow brought by heavy rainfall. The deposition rate DR is computed using Eqs. (4) and (5) by taking the value of the wetland’s area of 1800 m2. The resulting DR is shown in Fig. 5. The DR varies from 13 to 174 g/m2 per h with the average of 63 g/m2 per h. The largest DR occurs around hours 7 and 8. The small amount of accumulated sediment at hour 9 was due to a reduction in the level of input SS at that time. 3.2. Settling pond The measured input and output SS at points C and D at the settling pond are plotted in Fig. 6. The input SS reached up to 0.15 g/l. The output SS was stable at a much lower figure /0.03 g/l. Using Eq. (1), the trapping efficiency is also calculated and presented in Fig. 6. The efficiency ranged from 49 to 95%, with an average of (709/36)%. To look at the amount of variation in trapping efficiency, we also compute efficiency for the other 3 days of data. The efficiencies are: (779/25), (749/
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Fig. 4. Measured water depth, speed, flow rate, input and output SS, and calculated trapping efficiency of the mangrove wetland in Indonesia.
28), and (719/30)%. Using the flow discharge of 0.15 m3/s, respectively, and the pond’s area of 2600 m2, the deposition rate was calculated and plotted in Fig. 7. It was found that the deposition rate was much less than that of the wetland in Indonesia, i.e. it varied from 10.5 to 19 g/m2 per h with the average of 14 g/m2 per h. This is possibly due to two factors. First, the much lower flow rate at the settling pond of 0.15 m3/s compared with that of the CMW where for most of the time the flow rate was above 1 m3/s. The low inflow rate produces a low input flux of sediment that is available for deposition. Second, the much larger area of the settling pond of 2600 m2 compared with that of the CMW of 1800 m2.
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Fig. 5. Deposition rate DR or the areal efficiency calculated for the wetland.
Fig. 6. SS measured and calculated trapping efficiency of the settling pond in Australia.
3.3. Discussion It is important to stress that we cannot make a direct comparison between the two types of pond (i.e. vegetated and non-vegetated) because in the two cases studied here, there are many differences other than vegetation. Area, water depth, water chemistry and flow rates also differ considerably between the two sites.
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Fig. 7. Deposition rate DR for the settling pond.
The trapping efficiency of the mangrove wetland may be limited partly due to the presence of turbulence generated by flow around the mangroves. Even though the plant retards flow by friction which promotes settlement (Yang, 1998), they also generate turbulence preventing sediment deposition. The faster the flow, the larger the turbulence. Furukawa and Wolanski (1996) found that at a flow velocity around 20 cm/s, the turbulent velocity was up to three times larger than the mean flow. In our experiment, the flow velocity was often close to this value and as a result the trapping efficiency was also low. Close to slack high tide, however, when the velocity dropped to much less than the 20 cm/s and the water reached its greatest depth, the effect of turbulence decreased, and allowed sediment particles to settle. As a consequence, the trapping efficiency increased. Note that the velocity threshold needed for generating significant turbulence varies for different vegetation. Anderson and Charters (1982) found that for a sub-tidal bushy plant Gelidium nudifrons , the threshold is as low as 6 cm/s, whereas the threshold for seagrass Thalassia testudinum is between 12 and 17 cm/s (Koch and Gust, 1999). In order to increase the trapping efficiency of this mangrove wetland, the flow should ideally be slow and deep. Slower flow can be achieved by reducing the aspect ratio as has been suggested by Chen et al. (1993). Meanwhile, in order to cope with this deep flow
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requirement, the use of Rhizopora spp. is more advantageous than the use of other species such as Avicennia spp. (McKee, 1993; Lee et al., 1996). The experiment conducted in the non-vegetated settling pond resulted in a high trapping efficiency but a relatively low areal deposition rate. This high trapping efficiency is achieved because of the flow rate was very low, i.e. Q /0.15 m3/s compared with over 1 m3/s for the vegetated CMW pond at Maros) and is less turbulent, i.e. the flow was deeper and much slower. The areal deposition rate may be low due to either low sediment load or because the settling area was excessive for the input flow rate. Low sediment load is associated with small flow rate. This areal deposition rate might be increased by either (i) increasing the sediment load (i.e. large flow rate) or (ii) reducing the area. To further investigate each of these possibilities, we will use the settling formulae of Raju et al. (1999). The formulae were derived for calculating efficiency of a settling pond. The formulae of Raju et al. (1999) for calculating the efficiency of a settling pond with an area A is stated as below: EFF 11:7(v=U)0:81 (A=bh)0:23 (D1=6 =n g1=2 )0:98
(6)
where v is the settling velocity, U is the average pond water velocity, b and h is the width and water depth at the approach channel, respectively, D is the water depth at the settling pond, n is the Manning’s coefficient, and g is the gravitational acceleration. We use Eq. (6) to examine how changing flow rate and area affect the areal deposition rate. Let us consider these cases in more detail.
3.3.1. Case (1) the effect of changing flow rate Q We modify Eq. (6) into: EFF K1 Q0:81
(7)
where K1 (Bv)0:81 (A=bh)0:23 D0:97 (1=n g1=2 )0:98
(8)
Here we have substituted into Eq. (6), the velocity U /Q/BD , in which B is the pond’s width. Increasing the flow rate Q by a factor of 10 while other parameters are kept constant in Eq. (7), i.e. K1 is constant, the EFF reduces to (1/10)0.81 /0.15, i.e. the EFF is reduced by a factor of 1/0.15/6.45 from its former value. Using these new values, we recalculate the areal deposition rate. Here we take the new Q /1.5 m3/s (ten times larger than the previous Q which is 0.15 m3/s) and the new value of the EFF is taken by multiplying the previous EFF plotted in Fig. 6 by 0.15. The resulting average deposition rate calculated using Eq. (5) becomes 15.6 g/m2 per h. This is slightly larger than the previous average deposition rate of 14 g/m2 per h, but considering the rough nature of the calculations, the difference may well be negligible.
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3.3.2. Case (2a) the effect of reducing the area of the pond by reducing the length L and keeping the width B constant To calculate the resulting deposition rate caused by a reduction in pond length, we rewrite Eq. (6) into: EFF K2 L0:23
(9)
where K2 (v=Q)0:81 (1=bh)0:23 D0:97 (1=n g1=2 )B1:04
(10)
Now, Eq. (9) states that reducing the length by a factor of 10 while keeping K2 constant results in the EFF of (1/10)0.23 /0.59, i.e. the EFF is reduced by a factor of 1/0.59 /1.69 to about 59% of its previous value. We recalculate the deposition rate using these new values of both EFF and area. We find the deposition rate becomes 823 g/m2 per h. This value is about six times larger than the previous deposition rate of 14 g/m2 per h.
3.3.3. Case (2b) the effect of reducing the area of the pond by reducing the width B and keeping the length L constant To calculate the resulting deposition rate caused by this reduction, we rewrite (2.6) into: EFF K3 B1:04
(11)
where v 0:81 1 0:23 0:97 1 1=2 0:98 0:23 g K3 D L Q bh n
(12)
From Eq. (11) a reduction in the width by a factor of 10 while keeping K3 constant results in low EFF of (1/10)1.04 /0.09, i.e. the EFF is reduced by a factor of 1/0.09 to about 9% of its previous value. We recalculate the deposition rate using these new values of both EFF and area. We find the deposition rate becomes 12.6 g/m2 per h. This value is slightly less than the previous deposition rate of 14 g/m2 per h. Results from case (2a) and (2b) show that reducing the pond’s length L has a greater effect on DR than reducing the width B . We can also demonstrate that this result is still valid by keeping the area constant while changing B and L . For instance, reducing the length by a factor of 2 hence increasing the width by a factor of 2, the deposition rate found was 26 g/m2 per h. On the other hand, increasing the length by a factor of 2 while reducing the width by a factor of 2, the deposition rate found was 7.4 g/m2 per h. This suggests that in order to improve the sediment trapping capacity, the length-to-width ratio or the aspect
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ratio should be made smaller. Note that this idea has been recommended earlier by various investigators such as Chen et al. (1993), Kadlec and Knight (1996) and Platzer and Netter (1994). Further, Platzer and Netter (1994) also suggested the minimum length of 5 m to be used for a wetland system. More recently, applying a numerical method for modeling type I sedimentation, Jin et al. (2000) showed that within a 5 m-length pond the removal efficiency is almost 2/3 of the total removal efficiency. Eq. (6) was validated by using data obtained from experiments in mangrove and settling ponds. The result is given in Table 1, in which minimum and maximum trapping efficiencies are given based on literature values of settling velocity and Manning coefficient. The lower trapping efficiencies occur when very low settling velocities occur, i.e. when very fine clay is involved. From Table 1, it can be seen that the observed efficiency lies close to the lower bound of calculated efficiency. This regime belongs to very fine sediment materials and is consistent with observations from both ponds in which very fine clay particles are observed to settle. It should be noted that the calculated efficiency can be as much as a factor 3/4 orders of magnitudes larger for silt particles as opposed to clay particles. From this it is apparent that in-situ measurements of settling velocity are crucial in the design of efficient settling ponds. More work is needed to develop formulae equivalent to Eq. (6) to increase sediment trapping efficiency in the design of a settling pond and, possibly, of a constructed mangrove wetland. Firstly, finding the optimal aspect ratio for a particular settling pond. Even though, it can be shown from Eq. (6) that efficiency is a linear function of this ratio, in practice, this ratio might not be enlarged indefinitely. A reduction in this ratio reduces may lead to the short-circuiting problem in which the incoming sediment particles do not have enough time to settle thereby lowering the efficiency (Crites, 1994). In order to avoid this problem and to calculate the minimum length of a pond by rearranging Eq. (6) for a certain trapping efficiency to be achieved, one has to estimate the smallest settling velocity of incoming effluent. The best estimate would be to measure the settling velocity in-situ. This will be the subject for the work in the future. Secondly, investigating the use of the above settling formulae for the vegetated constructed wetland systems is recommended. This is possible through the use of appropriate roughness factor, i.e. the Manning’s coefficient n. One way of calculating this coefficient for the case of natural mangrove forest is available in Wolanski et al. (1992). The success of applying or possibly modifying Eq. (6) for the wetland might bring us to better understand why a wide range of width-to-length ratios have been suggested in the literature for wetland design (Knight et al., 1993; Brown and Reed, 1994; Kadlec and Knight, 1996). The observed efficiencies at both sites are based on mean values. The minimum and maximum calculated efficiency is found using the settling velocity w of 24 / 107 and 24/105 m/s (Heltzel and Teeter, 1987), respectively. The velocity covers clay and silt material commonly found in both sites. The Manning coefficient n used in the mangrove wetland and the settling pond is 0.2 s/m1/3 (Wolanski et al., 1992) and 0.02 s/m1/3 (Chow, 1959), respectively.
310
Site
Water Depth velocity (m) (m/ s)
Manning roughness Area Trapping efficiency (s/ m 1/3) (m2) (%) Observed Calculated using Eq. (6) with w of 24/10 7 and 24/10 5 m/s
CMW Non-vegetated settling pond
0.02 0.005
0.4 /0.8 0.2 1.0 0.02
1800 (309/25) 2600 (749/28)
Minimum
Maximum
21.3 91
888 3787
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Table 1 Trapping efficiency comparison between observation and calculation using formulae Eq. (6)
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4. Conclusion The sediment removal capacity of a CMW and a settling pond from tropical aquaculture ponds has been investigated. The CMW and the settling pond are intended to improve water quality of the influent and effluent, respectively. The trapping capacity observed in the wetland could be improved by slowing the water speed to less than 10 cm/s. Higher speed promotes turbulence, which is in turn, hinders the sediment deposition. The higher sediment retention found in the settling pond is achieved through deeper and slower water flow. It is likely that efficiency of the pond would be improved by using a shorter but wider pond.
Acknowledgements We thank the Director of RICA pond in Maros, South Sulawesi Indonesia for letting us its constructed mangrove wetland. We also would like to thank three anonymous reviewers, Professor D. Karoly and Dr A.D. Reyes Jr. for their suggestion to put uncertainty and to validate our results. HH would like to express his gratitude to the Australian Government for the AUSAID Ph.D. scholarship. Lead author Dr Halmar Halide is now teaching in the Physics Department, Hasanuddin University Jl, Kandea 26 Baraya, Sulawesi Selatan, Makassar 90153, Indonesia. Co-authors are obliged for the lead author’s re-working of the manuscript at his home institution, without any access to internet. HH is now making a weekly trip to an internet cafe´ and searching for work in meteorological forecasting with his fuzzy logic tools, via [email protected]
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