Dissolved Oxygen (DO) is one of the most important factors
affecting most aquaculture species. For fish culture, maintaining dissolved
oxygen at a level suitable for fish survival and growth does pond management.
When DO levels in aquaculture ponds become low, the cultured organisms
may become stressed or even die. A healthy balance pond provides a fluctuation
in oxygen levels between day and night that leaves an adequate concentration
of oxygen in the water that can support aquatic animal life during both
day and night hours. Phytoplankton can exert a profound effect on water
quality constituents, especially dissolved oxygen, by producing supersaturated
concentrations during the day and reduced levels during the night due
to biotic respiration and chemical oxidants result in a net loss of oxygen
which can reach critically low concentrations (Muhammetoglu and Soyupak,
2000). The highest oxygen levels in a pond are usually measured on sunny
afternoon when phytoplankton and other aquatic plants are producing oxygen
through photosynthesis. The lowest level occurs just before daybreak after
a night of oxygen consumption by aquatic plants and animals. Dissolved
oxygen consumption and regeneration by phytoplankton is directly related
to their rates of photosynthesis and respiration.
The intensity of solar radiation strongly regulates rates
of photosynthesis and oxygen evolution in fishponds (Romaire and Boyd,
1979). The rate of oxygen production is a function of the concentration
of algae and other forcing functions. Because the growth of algae is light
and temperature dependent, hence the rate of photosynthetic oxygen production
follows the same pattern. Temperature is a parameter that shows a marked
seasonal and daily variation in fishponds. It influences photosynthesis,
growth of algae and bio decomposition of organic matter in the pond. Other
factors such as density of fish, turbidity, organic matter levels and
wind velocity also greatly influence dissolved oxygen budgets for fishponds
(Boyd et al., 1978). In ponds showing marked stratification, surface
waters may be harmful to fish due to supersaturated DO conditions in combination
with high temperatures, while in the same pond near anoxic conditions
may exist close to the bottom (Chang and Ouyang, 1988; Losordo, 1988;
There are several reports of DO models incorporating
mechanistic characterization of the chemical, physical and biological
processes in an open pond which governs the resulting DO levels (Losordo,
1988; Losordo and Piedrahita, 1991). The intent of the present study describes
to develop a dissolved oxygen model using input variables in low cost
greenhouse fishpond. In this study additional modification have been implemented
to the predictions of DO performance in greenhouse fishpond from calculation
of solar radiation falling on greenhouse canopy cover to the pond water
surface. This model simulates the hourly variation of DO in a fishpond
over a 24 h period as influenced by the consumption and production of
oxygen by phytoplankton, fish and detritus. Measurable rates of photosynthesis
and respiration are needed for proper calibration of the model. This model
is neither site nor species specific and input variables can be adjusted
to accommodate most pond conditions. In freshwater fishponds effects of
solar radiation on temperature and oxygen variations have been described
in detail (Boyd, 1979) but relatively very little is known about the effect
of solar radiation on temperature and DO variations in greenhouse fishponds
(Tiwari et al., 2006; Ghosh et al., 2007). The model was
developed with the following objectives under consideration:
||To establish conceptual framework which unifies theories from various
||To determine components which have greatest effect on DO.
||To simulate the DO model to various alterations of pond depth and
Secchi disc depth.
DISSOLVED OXYGEN MODEL
The modeling of dissolved oxygen concentrations in an
aquaculture pond depend upon some factors, which contributes oxygen entry
into the pond, oxygen removal from the pond and oxygen exchanges within
the pond. In a pond, dissolved oxygen concentrations depends on the balance
between photosynthetic production, total respiration and exchanges with
atmosphere (Eq. 2)
Two main hypotheses of the DO models are as follows:
||It is assumed that pond water is completely mixed.
||Biomasses and nutrients are supposed to be constant throughout the
period during which the model is applied.
Under these two assumptions, the only state variable
of the system is mean dissolved oxygen concentrations and the only forcing
variables are solar intensity and temperature. Wind speed remains constant
throughout the experiment.
The solar radiation at the surface of the water attenuates
through the water column. The effective light intensity in the water column
directly affects the phytoplankton population, which in turn, increases
dissolved oxygen during the day via photosynthesis and utilizes oxygen
at night through respiration. Decaying phytoplankton, unconsumed fish
feed and fish waste products also decrease DO as represented by sediment
oxygen demand. The oxygen mass balance equations are specified by the
program or calculated hourly over a 24 h period.
The rate of change in DO concentration in fishpond
P = DO production, C = Consumption, E = Exchanges
|| = Rate of photosynthetic production by phytoplankton.
|| = Rate of DO respiration by phytoplankton.
|| = Rate of DO respiration by fish.
|| = Water column respiration.
|| = Sediment respiration rate.
|| = Rate of diffusion.
As wind velocity inside greenhouse remained negligible,
so exchange of oxygen in air water column through reaeration was negligible.
Thus, overall equation accounting for all processes involving production
and utilization of DO in greenhouse fishpond is as follows:
where, DO2D = 0
Though a small shallow pond is roughly horizontally uniform
(Wei and Laws, 1989), temperature and DO show a daily stratification,
which is destroyed by convective heat, exchanges during the night (Chang
and Ouyang, 1988).
After calculating oxygen concentration for each element
at each time step, the net oxygen change is then added to or subtracted
from the previous time step`s oxygen concentration. DO concentrations
can be calculated at any time (t) as:
Dot = DOt -1 +
|| = DO concentration at time t
|| = DO concentration at time t-1
||= rate of change in DO concentration during the time interval
FACTORS INFLUENCING PHOTOSYNTHETIC OXYGEN PRODUCTION
The total solar radiation reaching the earth`s surface is the sum
of direct solar radiation and diffuse radiation. After knowing beam and
diffuse radiation on horizontal surface Liu and Jordan (1963) have given
a formula to evaluate total radiation on a surface of arbitrary orientation:
It = Ib Rb
+ Id Rd + Ï?Rr (Ib+Id)
|| = Total solar radiation
|| = Beam solar radiation
|| = Diffuse solar radiation
|| = Conversion factor for beam radiation
|| = Conversion factor for diffuse radiation
|| = Conversion factor for reflected radiation
|| = Reflection for coefficient
Cos Î¸i = (Cos Ï†
Cos Î² + Sin Ï† SinÎ² CosÎ³) Cos Î´ Cos Ï‰
Cos Î´ Sin Ï‰ SinÎ² SinÎ³ +SinÎ´ (SinÏ†
CosÎ² -Cos Ï† SinÎ² Cos Î³)
Cos Î¸z = Cos Ï†
Cos Î´ Cos Ï‰ + Sin Î´ Sin Ï†
|| = Slope (angle between plane surface and horizontal)
|| = Angle of incidence
|| = Declination
|| = Angle of azimuth
|| = Hour angle
|| = Latitude
In order to determine total solar radiation on greenhouse
cover. It is necessary to determine total solar radiation on each curved
surface and wall of greenhouse by using Eq. 5. There
are two walls and 6-curved surface (I = 8). Hence, total solar radiation
on greenhouse canopy cover can be obtained by using expression
where Ai and Ii are an area of
ith section and solar radiation available on ith section.
Total solar radiation on water surface is computed using
Matlab 7.0 Programme.
Light intensity within the water column can be evaluated by Beer-Lambert
law, where the light intensity is attenuated exponentially with depth.
The light extinction coefficient is influenced by the absorption and scattering
of light within the water column dissolved and suspended substances of
biological and non-biological.
Light extinction coefficient can be calculated from relationship
Iz = Io. e-KZ,
or K = ln Io-ln Iz/Z
|| = Light intensity at depth Z (Wm-2)
|| = Photosynthetically active radiation just under water surface
||= Light extinction coefficient (m-1)
||= Depth of water (m)
Light attenuates faster in pond water with larger Ks.
Only 46% of the total solar radiation is available for photosynthesis
The Photosynthetically Active Radiation (PAR) is calculated
Photosynthetic Oxygen Production
In most aquaculture ponds phytoplankton provide the major source and
sink for dissolved oxygen. Gross phytoplankton production rates are affected
by many factors, including intensity of PAR, light attenuation in the
water column, water temperature, pH and dissolved nutrients concentrations.
Numerous expressions relating photosynthetic oxygen production
are available (Eilers and Peeters, 1988). Among these we have choosen
the Smith (1936) and Talling (1957) widely employed expression to estimate
the rate of phytoplankton oxygen production.
||= Maximum of DO production vs light curve (mg O2
|| = Saturated light intensity (W m-2)
|| = Initial slope of the DO production vs light carve (mg O2
L-1 (W m-2) h-1)
FACTORS INFLUENCING DO CONSUMPTION
In an aquaculture pond after sunrise, DO increases due to photosynthesis,
but at night, biotic respiration and chemical oxidants result in a net
loss of oxygen, which can reach critically, low concentrations. Loss of
oxygen from fishpond is due to fish respiration, plankton respiration,
water column respiration and sediment respiration.
Photo respiration occurring during photosynthesis is used to oxidize
organic components and is assumed to be proportional to gross photosynthesis.
The rate of Photo respiration is considered to be 10%
of the rate of photosynthetic oxygen production (Losordo, 1988).
DO2pr = Rate of Photo respiration
According to Boyd et al. (1978) respiration rate
for phytoplankton can be calculated
|| = Secchi disc depth (m)
|| = Water temperature ( Â°C)
The oxygen consumption of fish is affected by many factors, including
water temperature and oxygen content, carbondioxide level, size of fish,
activity and photoperiod. Using respiration flasks, researchers have been
able to establish relationships among various factors and minimum dissolved
oxygen requirements are known for many fish species (Davis, 1975). Andrews
and Matsuda (1975) studied the effect of water temperature, fish weight
and dissolved oxygen on fish respiration; Boyd (1979) performed a multiple
regression and developed an equation for catfish respiration using average
fish weight, water temperature and dissolved oxygen as variables. These
(Losordo, 1988) where
(Boyd, 1990) where
|| = Total fish biomass (kg)
|| = Av. Fish biomass concentration (kg m-3)
||= Area of pond (m2)
|| = Fish respiration (mg O2/kg/h)
|| = Av. wt. of fish (g)
Water Column Respiration
The water column respiration rate in the current model includes respiration
of the phytoplankton, zooplankton respiration and respiration by suspended
bacteria. Losordo (1980) found that water column respiration accounted
for on average; about 60% of the overnight DO decrease in the Auburn pond
could be attributed to the plankton respiration rate.
Water column respiration is calculated as:
||= Measured water column respiration rate (mg O2
|| = Temperature ( Â°C) at which water column respiration rate
Sediment Respiration Rate
Sediment respiration in the DO model includes decaying phytoplankton;
unconsumed fish feed and fish waste products, which require oxygen to
decompose. The night dissolved oxygen decline estimation for water column
respiration rates are considered to include that respiration exhibited
by the pond bottom sediments as well. When dissolved oxygen fall below
a certain threshold value (1.0 mg L-1) sediment respiration
was assumed to decline to zero.
Sediment respiration is calculated as (Losordo, 1988)
||= Measured sediment respiration rate at measured temp
(mg O2 L-1 h-1)
|| = Thickness of sediment (m)
MATERIALS AND METHODS
The experiment was conducted in a low cost Quonset shape greenhouse
(modified IARI model) having length 4.5 m, width 3.0 m and central height
of 2.0 m. The effective floor area of greenhouse enclosure is 13.5 m2.
The total surface area of UV stabilized canopy cover (0.2 mÎ¼) is
about 38 m2. The orientation of the greenhouse was from east
to west direction in order to allow maximum solar radiation inside greenhouse
during winter season. The volume of the greenhouse enclosure was 19 m3.
The frame of the greenhouse was constructed from Aluminum flat and is
covered by UV stabilized low density polythene film.
Experimental Set Up
The experiment was carried out at Solar Energy Park, IIT Delhi (Latitude
- 28 Â°35â€™, Longitude-77 Â°12â€™E and an altitude of 216
m above mean sea level). One rectangular low cost pond of size (4x2 m)
was constructed having a surface area of 8 m2 and an average
depth of 1m. The effective water volume is 8 m3 with water
depth level maintained at 1 m. The pond was filled with oxygenated ground
water. Aeration was not provided during the study period. The pond was
prepared as per the recommended practices (APHA, 1992). For healthy phytoplankton
production 8 g/8 m3 single super phosphate was added to both
the ponds every fortnightly. The pond was stocked with 40 No. Indian Major
Carp such a Catla catla (av. wt. 2.45 Â± 0.27 g; av. length
74 Â± 3 mm) and Labeo rohita (av.wt. 2.53 Â± 0.19 g;
av. length 76 Â± 2 mm) in the ratio of 3:1 in each pond. The fish
were fed primarily with supplementary feed (ground nut oil cake 50 and
rice bran 50%) at the rate of 2% live fish biomass twice a day throughout
the 150 days experimental period. Water samples were analyzed by standard
methods (APHA, 1992). The fish sampling was done once in 15 days interval
throughout the experimental period to measure length and weight for each
During the experimentation solar radiation (total and diffuse) on
a horizontal water surface on an hourly basis was measured by a dataloger
model R10 (least count Â± 10 W m-2) provided with probes.
The data loger was fitted with IBM type PC for further processing using
MATLAB 7 software. The average radiation based on 10 h duration sunshine
was taken into consideration. Water temperature at surface (0 to 4 cm)
was measured with the help of calibrated thermocouples on an hourly basis.
Dissolved oxygen was measured at hourly intervals from sunrise to next
day until after sunrise (Until DO started to increase) from four sites
in the water column. DO was measured using an YSI model 54 oxygen meter
(least count Â± 0.1 mg L-1) with a field probe with stirrer.
The wind velocity inside greenhouse was measured with a portable digital
anemometer with least count is 0.1 m sec-1. Data were analyzed
using the Statistical Analysis System (SAS) package (1982). Secchi disc
transparencies were estimated with a 20 cm diameter disk with alternate
black and white quardrants. The measurements were taken between 8:00 and
8:30 h and adjusted to midday values with a correction factor computed
from data presented by Almazan and Boyd (1978). The Secchi disc is lowered
to the point where disappears and rose until it just reappears. The average
of those two depths is called Secchi disc depth (SDD). Once a day at 11:00
h a water sample was taken from pond for measurement of chlorophyll-a.
The absorbance of the chlorophyll-a extract was read on a Beckman DU-7
spectrophotometer. From absorbance chlorophyll-a was estimated by Jeffrey
and Humphrey (1975). Water samples were poured into 300ml BOD bottles
(three replicates) and incubated in the dark for 4 h. Respiration rate
was calculated by measuring oxygen levels before and after incubation
for a known period of time. DO was measured using a YSI model 54 oxygen
meter with a BOD probe equipped with a stirrer.
The sediment cores with overlying water are collected
in glass tubes with rubber corks and their oxygen contents measured. After
a given period of incubation, the oxygen levels are again measured and
the differences between the two indicate the sediment oxygen consumption
rates. All experimental data (water temperature, solar radiation, dissolved
oxygen, secchi disc depth, phytoplankton biomass and chlorophyll-a content)
were measured from four sides of the ponds and taken average value.
Coefficient of Correlation (R)
When predicted values are validated with the experimental data then
correlation between predicted and experimental values is presented with
a coefficient known as coefficient of correlation. The coefficient of
correlation can be evaluated with the following expression (Chapra and
R = Coefficient of correlation
Root Mean Square of Percent Deviation (e)
The prediction is done with the help of thermal modeling. The predicted
values are validated with experimental data. The closeness of predicted
values and experimental data can be presented in terms of root mean square
of percent deviation. The expression used for this purpose is as follows
(Chapra and Canale, 1989).
e = Root mean square of per cent deviation
RESULTS AND DISCUSSION
The developed Dissolved oxygen model has been solved
with the help of a computer program based on Excel software. To verify
the accuracy of the developed model, experimental validations were conducted
for a typical winter day, 10th January, 2007. The hourly variation of
total solar radiation, dissolved oxygen, phytoplankton concentrations,
temperature and secchi disc depth were used as inputs to calibrate the
DO model. The coefficients and constants were used during model calibration
are presented in Table 1. The equations describing solar
radiation Eq. (5-11) are based in theoretical and experimental
basis and it has been applied to calculate total solar radiation falling
on greenhouse fishpond water surface.
From the Fig. 1 it is seen that maximum
solar radiation occurs at 12.00-13.00 h. The photosynthetic oxygen production
is plotted against PAR of the greenhouse pond during sunshine hours in
|| Hourly variations of total solar radiation (W m-2)
during sunshine hours in pond water surface
||Relation between photosynthetic oxygen production and
photoactive solar radiation (PAR, W m-2) during sunshine
|| Model parameters used for computation
|| Predicted model and experimental dissolve oxygen profiles
in greenhouse fishpond
Linear regression analysis showed that the model derived
from Smith (1936) and Talling (1957) fitted the experimental data very
well with coefficients of determination. It was positively correlated
with solar radiation and maximum production was at higher solar intensity.
The pond DO concentration increases as the phytoplankton produce more
oxygen through photosynthesis than is consumed through respiration and
decay. The phytoplankton photosynthesis decreases as the intensity of
the solar radiation decreases in the late afternoon. Algae respond to
the daily solar radiation and will reach their maximum rate of photosynthesis
at a light intensity, which is a function of the daily solar radiation
From the Fig. 3, it is noted that the
DO reaches its maximum between 15:00 to 17:00 of sunshine hours, while
the minimum values were observed at dawn between 5:00 to 7:00 h. The hourly
predictions of DO concentrations are very close to the experimental values.
The predicted DO exhibited good agreement with the values of coefficient
of correlation r = 0.99 and root mean square percent deviation e = 3.73%.
In the morning hours the pond DO is falling due to the phytoplankton,
fish respiration and detrital decay.
The model output has good correlation with the experimental
data at a correlation coefficient of (0.98). Such an agreement of the
predicted model data and that of the actual experimental data indicate
that the rates and constants used in the development of the model are
valid for a description of the processes of utilization and production
of DO (Fig. 4).
Simulations were carried to present the effect of pond
depth such as 0.5, 1.0 and 1.5 m and SDD of 0.5, 1.0 and 1.5 m on DO production
with constant phytoplankton Chl-a concentration of 232 Î¼g L-1.
For the pond with the simulated depth of 0.5m where SDD is 0.25 m produces
better results in terms of maximum DO production compared to 0.5 m SDD
during 24 h cycles (Fig. 5). Increasing the SDD resulted
in a drop in phytoplankton concentration and overall drop in DO production.
Therefore, DO concentrations in the 0.5 m deep pond with a SDD of 0.5
m were lower than 0.25 m SDD (Fig. 5).
|| Linear regression analysis of experimental and predicted
model DO for greenhouse fishpond
For the pond of simulated depth equal to 1.0 m and SDD
of 0.25 m yields most satisfactory DO concentration than 0.5 m and 1.0
m SDD. For 0.25 m SDD dissolved oxygen difference between sunshine hours
and off sunshine hours is comparatively more than 0.5 m and 1.0 m SDD.
Dissolved oxygen concentrations in the 1.0 m pond was lowest for 0.5 m
SDD and enhanced slightly when SDD increases to 1.0 m (Fig.
6). Increase in DO production caused by the rise in light penetration
with the same phytoplankton concentrations of 232 Î¼g L-1
Chl-a and with same fish and sediment respiration in both the ponds.
Dissolved oxygen production in the 1.5 m pond was lower
throughout the 24 h cycles for all SDD (0.5, 1.0 and 1.5 m) values due
to low phytoplankton Chl-a concentration (Fig. 7). DO
concentration was not reached above 7.5 mg L-1 and drops to
6.3 mg L-1 at the end of the 24 h simulation. The low DO values
in the shallow pond (0.5 m) with a high SDD (0.5 m) when compared to other
ponds with SDD equal to their pond depths (Fig. 6 and
7), was the result of the low overall oxygen production
relative to the sediment and fish respiration.
It can be shown from Fig. 8 that with
increase of light extinction (0.25, 0.50, 1.0 and 1.5) coefficient, pond
DO production level also decreases.
The effect of water temperature (10, 15, 20 and 25 Â°C)
on DO level (Fig. 9) shows that during sunshine period
there was a slight DO difference observed but off sunshine period significant
DO difference prevailed due to higher respiration rate. Respiration rate
increases with increase in water temperature.
|| Effect of Extinction coefficient (K) on DO level in
Figure 10 represents that pond DO
level reduces with increase of fish yield due to increase of respiration
rate by fish.
The relation between oxygen production and consumption
with the increase in Chl-a concentration show that the production and
consumption of oxygen were linearly correlated with the increase in Chl-a
concentration (Fig. 11). This model predicts that intermediate
|| Effect of water temperature on DO level in greenhouse
||Relation between oxygen production and oxygen consumption
with chlorophyll-a concentrations
(232 Î¼g L-1 Chl-a) produce the highest
levels of DO at dawn. Romaire and Boyd (1979) reported intermediate algal
densities i.e., 250 mg m-3 Chl-a produce the highest levels
of DO at dawn. The average concentrations of phytoplankton algal biomass
in greenhouse pond was 15.51 mg L-1. Based on DO model, the
computed value of DO produced 0.35 mg DO/mg dry algae, which is equivalent
to 23.15 mg DO/mg Chl-a. This parameter helps an aquaculturist to determine
DO from dry weight or Chl-a. Kayombo et al. (2000) obtained a rate
of DO production 1.59 mg mg-1 of dry algal biomass in waste
On the basis of the present study it can be concluded
that the developed computer model in this study is quite simple and reliable
to predict the DO concentrations for greenhouse pond. This model is valid
for new greenhouse pond, but it may be extended to old greenhouse pond.
Solar radiation sub model provides an effective means of using limited
data to estimate total solar radiation in greenhouse system. It may suggest
that for a balanced system, the amount of DO produced by the photosynthesis
process is enough to keep the system healthy. Oxygen was utilized mainly
due to plankton respiration. Model outputs resulted simple changes in
pond management parameters such as pond water depth and secchi disc depth.
DO concentrations in the pond varied with both pond depth and SDD. Dissolved
oxygen calculation method is relatively simple and easy to couple with
a water quality model. Overall, this model is very useful to determine
DO concentrations from algal photosynthetic performance in different pond
The authors gratefully acknowledge for financial assistance
provided by Department of Science and Technology, Government of India
to carry out this experiment. The authors are also thankful to Dr. Mrs.
Vinita Sharma, Scientist F, Department of Science and Technology, Government